library(ggeffects)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(ggpubr)
## Loading required package: ggplot2
library(grid)
library(lmerTest)
## Loading required package: lme4
## Loading required package: Matrix
## Warning: package 'Matrix' was built under R version 4.0.5
## Registered S3 methods overwritten by 'lme4':
##   method                          from
##   cooks.distance.influence.merMod car 
##   influence.merMod                car 
##   dfbeta.influence.merMod         car 
##   dfbetas.influence.merMod        car
## 
## Attaching package: 'lmerTest'
## The following object is masked from 'package:lme4':
## 
##     lmer
## The following object is masked from 'package:stats':
## 
##     step
library(devtools)
## Loading required package: usethis
## Warning: Can't find generic `testthat_print` in package testthat to register S3 method.
## Can't find generic `testthat_print` in package testthat to register S3 method.
## Can't find generic `testthat_print` in package testthat to register S3 method.
## ℹ This message is only shown to developers using devtools.
## ℹ Do you need to update testthat to the latest version?
source_url("https://raw.githubusercontent.com/JacobElder/MiscellaneousR/master/corToOne.R")
## SHA-1 hash of file is 07e3c11d2838efe15b1a6baf5ba2694da3f28cb1
source_url("https://raw.githubusercontent.com/JacobElder/MiscellaneousR/master/plotCommAxes.R")
## SHA-1 hash of file is 374a4de7fec345d21628a52c0ed0e4f2c389df8e
fullLong1 <- data.table::fread("~/Google Drive/Volumes/Research Project/Identities to Traits/Study 1/Cleaning/Output/id2traitDf.csv")
orderDf1 <- data.table::fread( "~/Google Drive/Volumes/Research Project/Identities to Traits/Study 1/Cleaning/Output/orderDf.csv")
idShort1 <- data.table::fread( "~/Google Drive/Volumes/Research Project/Identities to Traits/Study 1/Cleaning/Output/id2traitShort.csv")
indDiff1 <- data.table::fread( "~/Google Drive/Volumes/Research Project/Identities to Traits/Study 1/Cleaning/Output/indDiff.csv")
idSim1 <- data.table::fread( "~/Google Drive/Volumes/Research Project/Identities to Traits/Study 1/Cleaning/Output/identitySimDf.csv")
fullLong2 <- data.table::fread("~/Google Drive/Volumes/Research Project/Identities to Traits/Study 2/Cleaning/Output/id2traitDf.csv")
orderDf2 <- data.table::fread( "~/Google Drive/Volumes/Research Project/Identities to Traits/Study 2/Cleaning/Output/orderDf.csv")
idShort2 <- data.table::fread( "~/Google Drive/Volumes/Research Project/Identities to Traits/Study 2/Cleaning/Output/id2traitShort.csv")
indDiff2 <- data.table::fread( "~/Google Drive/Volumes/Research Project/Identities to Traits/Study 2/Cleaning/Output/indDiff.csv")
idSim2 <- data.table::fread( "~/Google Drive/Volumes/Research Project/Identities to Traits/Study 2/Cleaning/Output/identitySimDf.csv")
# subset data for traits to only appear once per subject

traitsPerS1 <- fullLong1 %>% distinct(subID, Idx, .keep_all = TRUE)
traitsPerS2 <- fullLong2 %>% distinct(subID, Idx, .keep_all = TRUE)

# subset data for only connected traits to appear per subject

connectDf1 <- fullLong1 %>% filter(connect==1)
connectDf2 <- fullLong2 %>% filter(connect==1)

# convert to factors

fullLong1$connect <- as.factor(fullLong1$connect)
levels(fullLong1$connect) <- list(No  = "0", Yes = "1")

fullLong2$connect <- as.factor(fullLong2$connect)
levels(fullLong2$connect) <- list(No  = "0", Yes = "1")

# pos neg asymmetry

idShort1$pndiff <- idShort1$pI2Tdeg - idShort1$nI2Tdeg
idShort2$pndiff <- idShort2$pI2Tdeg - idShort2$nI2Tdeg

Identity Typicality

Traits that are nominated as typical of some identity are evaluated more self-descriptively

Study 1

connect1 <- lmer(scale(selfResp) ~ connect + scale(subTend) + scale(traitTend) + ( connect | subID ) + ( 1 | traits), data=fullLong1)
summary(connect1)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(selfResp) ~ connect + scale(subTend) + scale(traitTend) +  
##     (connect | subID) + (1 | traits)
##    Data: fullLong1
## 
## REML criterion at convergence: 1399366
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.2593 -0.6864 -0.0184  0.6681  3.7715 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr 
##  traits   (Intercept) 0.14191  0.3767        
##  subID    (Intercept) 0.04058  0.2014        
##           connectYes  0.05296  0.2301   -0.26
##  Residual             0.64345  0.8022        
## Number of obs: 582288, groups:  traits, 296; subID, 246
## 
## Fixed effects:
##                   Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)       -0.01503    0.02541 463.81941  -0.591    0.554    
## connectYes         0.18925    0.01554 249.85050  12.181   <2e-16 ***
## scale(subTend)     0.01563    0.01246 243.17929   1.255    0.211    
## scale(traitTend)   0.40080    0.02192 294.05314  18.283   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) cnnctY scl(sT)
## connectYes  -0.129               
## scal(sbTnd)  0.000 -0.015        
## scl(trtTnd)  0.000 -0.003  0.000
connect1.plot <- ggpredict(connect1, c("connect")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Identity-Typicality") + ylab("Self-Evaluation")
connect1.plot

Study 2

connect2 <- lmer(scale(selfResp) ~ connect + subTend + traitTend + ( connect | subID ) + ( 1 | traits), data=fullLong2)
summary(connect2)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(selfResp) ~ connect + subTend + traitTend + (connect |  
##     subID) + (1 | traits)
##    Data: fullLong2
## 
## REML criterion at convergence: 1407917
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.8388 -0.6677 -0.0131  0.6664  4.1796 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr 
##  traits   (Intercept) 0.16014  0.4002        
##  subID    (Intercept) 0.06177  0.2485        
##           connectYes  0.03923  0.1981   -0.33
##  Residual             0.63392  0.7962        
## Number of obs: 589488, groups:  traits, 296; subID, 249
## 
## Fixed effects:
##               Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept) -6.079e-01  4.866e-02  4.350e+02 -12.492   <2e-16 ***
## connectYes   1.664e-01  1.356e-02  2.451e+02  12.271   <2e-16 ***
## subTend      7.096e-04  6.751e-04  2.439e+02   1.051    0.294    
## traitTend    9.511e-01  5.995e-02  2.940e+02  15.866   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) cnnctY subTnd
## connectYes -0.092              
## subTend    -0.322 -0.024       
## traitTend  -0.750 -0.003  0.000
connect2.plot <- ggpredict(connect2, c("connect")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Identity-Typicality") + ylab("Self-Evaluation")
connect2.plot

Combined

plotCommAxes(connect1.plot, connect2.plot, "Connect", "Self-Evaluation")

Effect of Identity Typicality Depends on Identity Importance

Study 1

Identity importance defined by strength of identification. This is not significant for identity-to-identity centrality.

connect.streng1 <- lmer(scale(selfResp) ~ connect * scale(streng) + subTend + traitTend + ( connect + scale(streng) | subID ) + ( 1 | traits), data=fullLong1)
## boundary (singular) fit: see help('isSingular')
summary(connect.streng1)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(selfResp) ~ connect * scale(streng) + subTend + traitTend +  
##     (connect + scale(streng) | subID) + (1 | traits)
##    Data: fullLong1
## 
## REML criterion at convergence: 1399343
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.2607 -0.6863 -0.0183  0.6682  3.7875 
## 
## Random effects:
##  Groups   Name          Variance  Std.Dev. Corr       
##  traits   (Intercept)   1.420e-01 0.376825            
##  subID    (Intercept)   4.056e-02 0.201385            
##           connectYes    5.157e-02 0.227097 -0.26      
##           scale(streng) 1.648e-05 0.004059  0.17 -1.00
##  Residual               6.434e-01 0.802125            
## Number of obs: 582288, groups:  traits, 296; subID, 246
## 
## Fixed effects:
##                            Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)              -6.642e-01  4.493e-02  4.035e+02 -14.783  < 2e-16 ***
## connectYes                1.860e-01  1.536e-02  2.503e+02  12.109  < 2e-16 ***
## scale(streng)            -4.934e-03  1.294e-03  2.805e+03  -3.813  0.00014 ***
## subTend                   6.173e-04  5.130e-04  2.433e+02   1.203  0.23005    
## traitTend                 9.622e-01  5.265e-02  2.937e+02  18.275  < 2e-16 ***
## connectYes:scale(streng)  2.740e-02  4.858e-03  2.185e+05   5.640  1.7e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) cnnctY scl(s) subTnd trtTnd
## connectYes  -0.065                            
## scal(strng)  0.008 -0.188                     
## subTend     -0.290 -0.015  0.006              
## traitTend   -0.772 -0.003  0.001  0.000       
## cnnctYs:s()  0.001 -0.039 -0.240 -0.004  0.000
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
connect.streng1.plot <- ggpredict(connect.streng1, c("streng","connect")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Strength of Identification") + ylab("Self-Evaluation")
connect.streng1.plot

Study 2

connect.streng2 <- lmer(scale(selfResp) ~ connect * scale(streng) + ( connect + scale(streng) | subID ) + ( 1 | traits), data=fullLong2)
## boundary (singular) fit: see help('isSingular')
summary(connect.streng2)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## scale(selfResp) ~ connect * scale(streng) + (connect + scale(streng) |  
##     subID) + (1 | traits)
##    Data: fullLong2
## 
## REML criterion at convergence: 1408086
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.8410 -0.6676 -0.0133  0.6669  4.1877 
## 
## Random effects:
##  Groups   Name          Variance  Std.Dev.  Corr       
##  traits   (Intercept)   2.966e-01 0.5446027            
##  subID    (Intercept)   6.202e-02 0.2490477            
##           connectYes    3.813e-02 0.1952748 -0.34      
##           scale(streng) 6.163e-07 0.0007851  0.62 -0.95
##  Residual               6.339e-01 0.7961835            
## Number of obs: 589488, groups:  traits, 296; subID, 249
## 
## Fixed effects:
##                            Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)              -1.229e-02  3.539e-02  4.278e+02  -0.347   0.7285    
## connectYes                1.658e-01  1.339e-02  2.445e+02  12.379  < 2e-16 ***
## scale(streng)            -2.572e-03  1.273e-03  3.250e+05  -2.021   0.0433 *  
## connectYes:scale(streng)  1.990e-02  4.873e-03  1.350e+05   4.083 4.44e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) cnnctY scl(s)
## connectYes  -0.143              
## scal(strng)  0.011 -0.035       
## cnnctYs:s()  0.000 -0.026 -0.252
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
connect.streng2.plot <- ggpredict(connect.streng2, c("streng","connect")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Strength of Identification") + ylab("Self-Evaluation")
connect.streng2.plot

Combined

plotCommAxes(connect.streng1.plot, connect.streng2.plot, "Strength of Identification", "Self-Evaluation")

Effect of Identity Typicality Depends on Size

Size Difference

connect.size2 <- lmer(scale(selfResp) ~ connect * scale(sizeD) + ( connect + scale(sizeD) | subID ) + ( 1 | traits), data=fullLong2)
## boundary (singular) fit: see help('isSingular')
summary(connect.size2)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(selfResp) ~ connect * scale(sizeD) + (connect + scale(sizeD) |  
##     subID) + (1 | traits)
##    Data: fullLong2
## 
## REML criterion at convergence: 1366642
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.8551 -0.6674 -0.0138  0.6668  4.1965 
## 
## Random effects:
##  Groups   Name         Variance  Std.Dev. Corr       
##  traits   (Intercept)  3.002e-01 0.54787             
##  subID    (Intercept)  6.220e-02 0.24941             
##           connectYes   3.937e-02 0.19841  -0.34      
##           scale(sizeD) 1.083e-06 0.00104   0.10 -0.97
##  Residual              6.325e-01 0.79532             
## Number of obs: 572620, groups:  traits, 296; subID, 247
## 
## Fixed effects:
##                           Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)             -1.358e-02  3.560e-02  4.276e+02  -0.381 0.703054    
## connectYes               1.672e-01  1.367e-02  2.436e+02  12.230  < 2e-16 ***
## scale(sizeD)            -2.311e-03  1.227e-03  3.716e+04  -1.883 0.059720 .  
## connectYes:scale(sizeD)  1.435e-02  4.187e-03  1.938e+05   3.426 0.000612 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) cnnctY scl(D)
## connectYes  -0.141              
## scale(sizD)  0.003 -0.050       
## cnnctYs:(D)  0.000 -0.022 -0.289
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
connect.size2.plot <- ggpredict(connect.size2, c("sizeD","connect")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Size Differences") + ylab("Self-Evaluation")
connect.size2.plot

Identity Connections

Traits that are nominated as typical of more identities are evaluated more self-descriptively

Study 1

moconn1 <- lmer(scale(selfResp) ~ scale(IdIn) + ( scale(IdIn) | subID ) + ( 1 | traits), data=traitsPerS1)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0195226 (tol = 0.002, component 1)
summary(moconn1)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(selfResp) ~ scale(IdIn) + (scale(IdIn) | subID) + (1 |  
##     traits)
##    Data: traitsPerS1
## 
## REML criterion at convergence: 174980.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.0715 -0.6794 -0.0191  0.6616  3.8040 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr 
##  traits   (Intercept) 0.26352  0.5133        
##  subID    (Intercept) 0.04077  0.2019        
##           scale(IdIn) 0.02463  0.1569   -0.12
##  Residual             0.62526  0.7907        
## Number of obs: 72786, groups:  traits, 296; subID, 246
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)   0.01208    0.03268 397.14217    0.37    0.712    
## scale(IdIn)   0.16488    0.01122 248.97431   14.69   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## scale(IdIn) -0.028
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.0195226 (tol = 0.002, component 1)
moconn1.plot <- ggpredict(moconn1, c("IdIn")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Identity-Associations") + ylab("Self-Evaluation")
moconn1.plot

Study 2

moconn2 <- lmer(scale(selfResp) ~ scale(IdIn) + ( scale(IdIn) | subID ) + ( 1 | traits), data=traitsPerS1)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0195226 (tol = 0.002, component 1)
summary(moconn2)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(selfResp) ~ scale(IdIn) + (scale(IdIn) | subID) + (1 |  
##     traits)
##    Data: traitsPerS1
## 
## REML criterion at convergence: 174980.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.0715 -0.6794 -0.0191  0.6616  3.8040 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr 
##  traits   (Intercept) 0.26352  0.5133        
##  subID    (Intercept) 0.04077  0.2019        
##           scale(IdIn) 0.02463  0.1569   -0.12
##  Residual             0.62526  0.7907        
## Number of obs: 72786, groups:  traits, 296; subID, 246
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)   0.01208    0.03268 397.14217    0.37    0.712    
## scale(IdIn)   0.16488    0.01122 248.97431   14.69   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## scale(IdIn) -0.028
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.0195226 (tol = 0.002, component 1)
moconn2.plot <- ggpredict(moconn2, c("IdIn")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Identity-Associations") + ylab("Self-Evaluation")
moconn2.plot

Combined

plotCommAxes(moconn1.plot, moconn2.plot, "Identity-Typicality", "Self-Evaluation")

Identity Overlap

Study 1

sm1<-lmer(scale(selfResp) ~ scale(T.Sim) * scale(streng) + scale(subTend) + scale(traitTend)   + ( scale(T.Sim) | subID ) + ( 1 | traits), data=orderDf1, control=lmerControl(optimizer="bobyqa",
                                 optCtrl=list(maxfun=2e5)))
summary(sm1)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(selfResp) ~ scale(T.Sim) * scale(streng) + scale(subTend) +  
##     scale(traitTend) + (scale(T.Sim) | subID) + (1 | traits)
##    Data: orderDf1
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2e+05))
## 
## REML criterion at convergence: 1385983
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.4786 -0.6759 -0.0208  0.6605  4.0231 
## 
## Random effects:
##  Groups   Name         Variance Std.Dev. Corr 
##  traits   (Intercept)  0.13072  0.3616        
##  subID    (Intercept)  0.04123  0.2030        
##           scale(T.Sim) 0.02107  0.1452   -0.21
##  Residual              0.62841  0.7927        
## Number of obs: 582288, groups:  traits, 296; subID, 246
## 
## Fixed effects:
##                              Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)                -5.056e-03  2.471e-02  4.758e+02  -0.205    0.838
## scale(T.Sim)                5.871e-02  9.387e-03  2.481e+02   6.254 1.74e-09
## scale(streng)              -5.543e-03  1.240e-03  5.738e+05  -4.469 7.86e-06
## scale(subTend)              1.069e-02  1.272e-02  2.439e+02   0.840    0.402
## scale(traitTend)            3.874e-01  2.104e-02  2.941e+02  18.409  < 2e-16
## scale(T.Sim):scale(streng)  6.828e-03  1.369e-03  5.422e+05   4.987 6.14e-07
##                               
## (Intercept)                   
## scale(T.Sim)               ***
## scale(streng)              ***
## scale(subTend)                
## scale(traitTend)           ***
## scale(T.Sim):scale(streng) ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) sc(T.S) scl(s) scl(sT) scl(tT)
## scale(T.Sm) -0.109                               
## scal(strng) -0.001 -0.016                        
## scal(sbTnd) -0.001 -0.009   0.007                
## scl(trtTnd)  0.000 -0.002   0.002  0.001         
## scl(T.S):() -0.003 -0.013   0.149  0.001   0.000
sm1.plot <- ggpredict(sm1, c("T.Sim", "streng")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Overlap with Identity") + ylab("Self-Evaluation")
sm1.plot

Study 2

sm2<-lmer(scale(selfResp) ~ scale(T.Sim) * scale(streng) + scale(subTend) + scale(traitTend)  + ( scale(T.Sim) | subID ) + ( 1 | traits), data=orderDf2, control=lmerControl(optimizer="bobyqa",
                                 optCtrl=list(maxfun=2e5)))
summary(sm2)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(selfResp) ~ scale(T.Sim) * scale(streng) + scale(subTend) +  
##     scale(traitTend) + (scale(T.Sim) | subID) + (1 | traits)
##    Data: orderDf2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2e+05))
## 
## REML criterion at convergence: 1388244
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.8282 -0.6618 -0.0138  0.6587  4.2663 
## 
## Random effects:
##  Groups   Name         Variance Std.Dev. Corr 
##  traits   (Intercept)  0.14988  0.3871        
##  subID    (Intercept)  0.06194  0.2489        
##           scale(T.Sim) 0.01701  0.1304   -0.22
##  Residual              0.62445  0.7902        
## Number of obs: 584752, groups:  traits, 296; subID, 247
## 
## Fixed effects:
##                              Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)                -5.726e-04  2.754e-02  5.069e+02  -0.021  0.98342
## scale(T.Sim)                4.703e-02  8.433e-03  2.485e+02   5.577 6.37e-08
## scale(streng)              -3.947e-03  1.250e-03  5.802e+05  -3.158  0.00159
## scale(subTend)              1.362e-02  1.553e-02  2.454e+02   0.877  0.38146
## scale(traitTend)            3.621e-01  2.253e-02  2.941e+02  16.075  < 2e-16
## scale(T.Sim):scale(streng) -1.321e-03  1.349e-03  5.184e+05  -0.979  0.32754
##                               
## (Intercept)                   
## scale(T.Sim)               ***
## scale(streng)              ** 
## scale(subTend)                
## scale(traitTend)           ***
## scale(T.Sim):scale(streng)    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) sc(T.S) scl(s) scl(sT) scl(tT)
## scale(T.Sm) -0.120                               
## scal(strng)  0.000 -0.010                        
## scal(sbTnd) -0.001 -0.008   0.002                
## scl(trtTnd)  0.000 -0.002   0.001  0.000         
## scl(T.S):() -0.002 -0.009   0.105  0.000   0.000
sm2.plot <- ggpredict(sm2, c("T.Sim", "streng")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Overlap with Identity") + ylab("Self-Evaluation")
sm2.plot

Combined Plot

plotCommAxes(sm1.plot, sm2.plot, "Identity Overlap", "Self-Evaluation")

Identity Distance

Study 1

dm1<-lmer(scale(selfResp) ~ scale(order) * scale(streng) + scale(subTend) + scale(traitTend)  + ( scale(order) | subID ) + ( 1 | traits), data=orderDf1)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00884777 (tol = 0.002, component 1)
summary(dm1)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(selfResp) ~ scale(order) * scale(streng) + scale(subTend) +  
##     scale(traitTend) + (scale(order) | subID) + (1 | traits)
##    Data: orderDf1
## 
## REML criterion at convergence: 1355429
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.3472 -0.6756 -0.0174  0.6693  4.1093 
## 
## Random effects:
##  Groups   Name         Variance Std.Dev. Corr
##  traits   (Intercept)  0.13692  0.3700       
##  subID    (Intercept)  0.03994  0.1999       
##           scale(order) 0.01384  0.1176   0.18
##  Residual              0.63517  0.7970       
## Number of obs: 566898, groups:  traits, 296; subID, 246
## 
## Fixed effects:
##                              Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)                -4.469e-03  2.503e-02  4.666e+02  -0.179  0.85834
## scale(order)               -6.429e-02  7.620e-03  2.474e+02  -8.438 2.74e-15
## scale(streng)              -4.114e-03  1.258e-03  5.599e+05  -3.272  0.00107
## scale(subTend)              1.298e-02  1.260e-02  2.445e+02   1.031  0.30373
## scale(traitTend)            3.937e-01  2.154e-02  2.944e+02  18.281  < 2e-16
## scale(order):scale(streng) -9.300e-03  1.281e-03  4.868e+05  -7.261 3.85e-13
##                               
## (Intercept)                   
## scale(order)               ***
## scale(streng)              ** 
## scale(subTend)                
## scale(traitTend)           ***
## scale(order):scale(streng) ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(r) scl(s) scl(sT) scl(tT)
## scale(ordr)  0.091                              
## scal(strng)  0.000  0.014                       
## scal(sbTnd)  0.000  0.005  0.007                
## scl(trtTnd)  0.000  0.002  0.001  0.000         
## scl(rdr):()  0.002  0.005 -0.053  0.001   0.001 
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00884777 (tol = 0.002, component 1)
dm1.plot <- ggpredict(dm1, c("order", "streng")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Distance from Identity") + ylab("Self-Evaluation")
dm1.plot

Study 2

dm2<-lmer(scale(selfResp) ~ scale(order) * scale(streng) + scale(subTend) + scale(traitTend)  + ( scale(order) | subID ) + ( 1 | traits), data=orderDf2)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0084477 (tol = 0.002, component 1)
summary(dm2)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(selfResp) ~ scale(order) * scale(streng) + scale(subTend) +  
##     scale(traitTend) + (scale(order) | subID) + (1 | traits)
##    Data: orderDf2
## 
## REML criterion at convergence: 1374845
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.0795 -0.6635 -0.0129  0.6634  4.2017 
## 
## Random effects:
##  Groups   Name         Variance Std.Dev. Corr
##  traits   (Intercept)  0.154257 0.39276      
##  subID    (Intercept)  0.061307 0.24760      
##           scale(order) 0.009124 0.09552  0.22
##  Residual              0.632151 0.79508      
## Number of obs: 576170, groups:  traits, 296; subID, 247
## 
## Fixed effects:
##                              Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)                -2.815e-03  2.776e-02  5.023e+02  -0.101 0.919263
## scale(order)               -5.488e-02  6.222e-03  2.491e+02  -8.820  < 2e-16
## scale(streng)              -2.985e-03  1.263e-03  5.720e+05  -2.364 0.018082
## scale(subTend)              1.459e-02  1.543e-02  2.445e+02   0.945 0.345468
## scale(traitTend)            3.623e-01  2.285e-02  2.940e+02  15.852  < 2e-16
## scale(order):scale(streng) -4.490e-03  1.218e-03  4.239e+05  -3.685 0.000229
##                               
## (Intercept)                   
## scale(order)               ***
## scale(streng)              *  
## scale(subTend)                
## scale(traitTend)           ***
## scale(order):scale(streng) ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(r) scl(s) scl(sT) scl(tT)
## scale(ordr)  0.121                              
## scal(strng)  0.000  0.008                       
## scal(sbTnd)  0.000  0.005  0.001                
## scl(trtTnd)  0.000  0.002  0.000  0.000         
## scl(rdr):()  0.000  0.001 -0.051  0.002   0.001 
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.0084477 (tol = 0.002, component 1)
dm2.plot <- ggpredict(dm2, c("order", "streng")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Distance from Identity") + ylab("Self-Evaluation")
dm2.plot

Combined Plot

plotCommAxes(dm1.plot, dm2.plot, "Identity Distance", "Self-Evaluation")

Identity Similarity and Distance Joint Influence on Self-Evaluations (Principal Component)

Study 1

pca1<-lmer(scale(selfResp) ~ scale(PCAdist) * scale(streng) + scale(subTend) + scale(traitTend) + ( scale(PCAdist) | subID ) + ( 1 | traits), data=orderDf1)
summary(pca1)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(selfResp) ~ scale(PCAdist) * scale(streng) + scale(subTend) +  
##     scale(traitTend) + (scale(PCAdist) | subID) + (1 | traits)
##    Data: orderDf1
## 
## REML criterion at convergence: 1348143
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.6482 -0.6714 -0.0188  0.6621  4.2617 
## 
## Random effects:
##  Groups   Name           Variance Std.Dev. Corr 
##  traits   (Intercept)    0.13138  0.3625        
##  subID    (Intercept)    0.04217  0.2054        
##           scale(PCAdist) 0.02132  0.1460   -0.24
##  Residual                0.62694  0.7918        
## Number of obs: 566898, groups:  traits, 296; subID, 246
## 
## Fixed effects:
##                                Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)                  -6.219e-03  2.483e-02  4.778e+02  -0.250    0.802
## scale(PCAdist)                7.259e-02  9.418e-03  2.480e+02   7.708 3.08e-13
## scale(streng)                -6.255e-03  1.259e-03  5.596e+05  -4.969 6.74e-07
## scale(subTend)                6.148e-03  1.277e-02  2.444e+02   0.481    0.631
## scale(traitTend)              3.839e-01  2.110e-02  2.942e+02  18.196  < 2e-16
## scale(PCAdist):scale(streng)  9.040e-03  1.314e-03  5.294e+05   6.878 6.06e-12
##                                 
## (Intercept)                     
## scale(PCAdist)               ***
## scale(streng)                ***
## scale(subTend)                  
## scale(traitTend)             ***
## scale(PCAdist):scale(streng) ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) sc(PCA) scl(s) scl(sT) scl(tT)
## scl(PCAdst) -0.126                               
## scal(strng)  0.000 -0.016                        
## scal(sbTnd)  0.000 -0.007   0.008                
## scl(trtTnd)  0.000 -0.003   0.002  0.001         
## scl(PCA):() -0.003 -0.003   0.103 -0.001   0.000
pca1.plot <- ggpredict(pca1, c("PCAdist", "streng")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Composite Identity-Overlap") + ylab("Self-Evaluation")
pca1.plot

Study 2

pca2<-lmer(scale(selfResp) ~ scale(PCAdist) * scale(streng) + scale(subTend) + scale(traitTend) + ( scale(PCAdist) | subID ) + ( 1 | traits), data=orderDf2)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0132975 (tol = 0.002, component 1)
summary(pca2)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(selfResp) ~ scale(PCAdist) * scale(streng) + scale(subTend) +  
##     scale(traitTend) + (scale(PCAdist) | subID) + (1 | traits)
##    Data: orderDf2
## 
## REML criterion at convergence: 1369642
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.6597 -0.6602 -0.0132  0.6597  4.2286 
## 
## Random effects:
##  Groups   Name           Variance Std.Dev. Corr
##  traits   (Intercept)    0.14953  0.3867       
##  subID    (Intercept)    0.06341  0.2518       
##           scale(PCAdist) 0.01572  0.1254   0.25
##  Residual                0.62633  0.7914       
## Number of obs: 576170, groups:  traits, 296; subID, 247
## 
## Fixed effects:
##                                Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)                  -2.851e-03  2.763e-02  5.093e+02  -0.103 0.917860
## scale(PCAdist)               -6.009e-02  8.097e-03  2.491e+02  -7.422 1.83e-12
## scale(streng)                -4.546e-03  1.263e-03  5.719e+05  -3.598 0.000321
## scale(subTend)                1.001e-02  1.557e-02  2.455e+02   0.643 0.520644
## scale(traitTend)              3.568e-01  2.250e-02  2.939e+02  15.854  < 2e-16
## scale(PCAdist):scale(streng) -2.598e-03  1.277e-03  5.077e+05  -2.034 0.041923
##                                 
## (Intercept)                     
## scale(PCAdist)               ***
## scale(streng)                ***
## scale(subTend)                  
## scale(traitTend)             ***
## scale(PCAdist):scale(streng) *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) sc(PCA) scl(s) scl(sT) scl(tT)
## scl(PCAdst)  0.144                               
## scal(strng)  0.000  0.009                        
## scal(sbTnd)  0.000  0.006   0.002                
## scl(trtTnd)  0.000  0.002   0.001  0.000         
## scl(PCA):()  0.001 -0.003  -0.079  0.002   0.001 
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.0132975 (tol = 0.002, component 1)
pca2.plot <- ggpredict(pca2, c("PCAdist", "streng")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Composite Identity-Overlap") + ylab("Self-Evaluation")
pca2.plot

Combined Plot

plotCommAxes(pca1.plot, pca2.plot, "Composite Identity-Overlap", "Self-Evaluation")

Identity Centrality Predicts Strength of Identification

Study 1

I2I1.streng<-lmer(scale(streng) ~ scale(I2Ideg) + ( scale(I2Ideg) | subID ) + ( 1 | id), data=idShort1)
## boundary (singular) fit: see help('isSingular')
summary(I2I1.streng)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(streng) ~ scale(I2Ideg) + (scale(I2Ideg) | subID) + (1 |  
##     id)
##    Data: idShort1
## 
## REML criterion at convergence: 5135.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.7156 -0.3367  0.1702  0.6259  2.1894 
## 
## Random effects:
##  Groups   Name          Variance  Std.Dev. Corr
##  subID    (Intercept)   0.1817128 0.42628      
##           scale(I2Ideg) 0.0007822 0.02797  1.00
##  id       (Intercept)   0.1633094 0.40412      
##  Residual               0.6767897 0.82267      
## Number of obs: 1968, groups:  subID, 246; id, 8
## 
## Fixed effects:
##                Estimate Std. Error        df t value Pr(>|t|)  
## (Intercept)   2.663e-03  1.466e-01 7.507e+00   0.018   0.9860  
## scale(I2Ideg) 6.071e-02  2.810e-02 3.183e+02   2.161   0.0315 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## scal(I2Idg) 0.018 
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
I2I1.streng.plot <- ggpredict(I2I1.streng, c("I2Ideg")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Identity-to-Identity Centrality") + ylab("Self-Evaluation")
I2I1.streng.plot

Study 2

I2I2.streng<-lmer(scale(streng) ~ scale(I2Ideg) + ( scale(I2Ideg) | subID ) + ( 1 | id), data=idShort2)
summary(I2I2.streng)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(streng) ~ scale(I2Ideg) + (scale(I2Ideg) | subID) + (1 |  
##     id)
##    Data: idShort2
## 
## REML criterion at convergence: 5022.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.6212 -0.3770  0.1274  0.6080  2.6012 
## 
## Random effects:
##  Groups   Name          Variance Std.Dev. Corr
##  subID    (Intercept)   0.21196  0.4604       
##           scale(I2Ideg) 0.01029  0.1014   0.24
##  id       (Intercept)   0.19078  0.4368       
##  Residual               0.61342  0.7832       
## Number of obs: 1976, groups:  subID, 247; id, 8
## 
## Fixed effects:
##                Estimate Std. Error        df t value Pr(>|t|)   
## (Intercept)   4.786e-03  1.582e-01 7.518e+00   0.030  0.97666   
## scale(I2Ideg) 7.761e-02  2.796e-02 1.494e+02   2.775  0.00622 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## scal(I2Idg) 0.020
I2I2.streng.plot <- ggpredict(I2I2.streng, c("I2Ideg")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Identity-to-Identity Centrality") + ylab("Self-Evaluation")
I2I2.streng.plot

Combined Plot

plotCommAxes(I2I1.streng.plot, I2I2.streng.plot, "Identity-to-Identity Centrality", "Strength of Identification")

Strength of Identification Predicts More Shared Traits Between Identities

Study 1

TSharedStreng1 <- lmer( scale(traitCommNod) ~ scale(streng) + ( scale(streng)  | subID) + ( 1 | id), data=idShort1)
summary(TSharedStreng1)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(traitCommNod) ~ scale(streng) + (scale(streng) | subID) +  
##     (1 | id)
##    Data: idShort1
## 
## REML criterion at convergence: 3387.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.8325 -0.5313  0.0625  0.6032  4.0899 
## 
## Random effects:
##  Groups   Name          Variance Std.Dev. Corr
##  subID    (Intercept)   0.778870 0.88254      
##           scale(streng) 0.018863 0.13734  0.51
##  id       (Intercept)   0.009726 0.09862      
##  Residual               0.201142 0.44849      
## Number of obs: 1968, groups:  subID, 246; id, 8
## 
## Fixed effects:
##                 Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)    -0.002885   0.067058  68.777738  -0.043    0.966    
## scale(streng)   0.065984   0.016600 204.721147   3.975 9.76e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## scal(strng) 0.215
TSharedStreng1.plot <- ggpredict(TSharedStreng1, c("streng")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Strength of Identification") + ylab("Shared Traits")

Study 2

TSharedStreng2 <- lmer( scale(traitCommNod) ~ scale(streng) + ( scale(streng)  | subID) + ( 1 | id), data=idShort2)
summary(TSharedStreng2)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(traitCommNod) ~ scale(streng) + (scale(streng) | subID) +  
##     (1 | id)
##    Data: idShort2
## 
## REML criterion at convergence: 3404.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.5612 -0.5208  0.0559  0.6180  3.5174 
## 
## Random effects:
##  Groups   Name          Variance Std.Dev. Corr
##  subID    (Intercept)   0.790216 0.88894      
##           scale(streng) 0.005263 0.07255  0.60
##  id       (Intercept)   0.006149 0.07842      
##  Residual               0.208387 0.45649      
## Number of obs: 1976, groups:  subID, 247; id, 8
## 
## Fixed effects:
##                Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)     0.00116    0.06385 107.71861   0.018 0.985539    
## scale(streng)   0.05655    0.01465 181.90740   3.860 0.000157 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## scal(strng) 0.165
TSharedStreng2.plot <- ggpredict(TSharedStreng2, c("streng")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Strength of Identification") + ylab("Shared Traits")

Combined Plot

ggpubr::ggarrange(TSharedStreng1.plot, TSharedStreng2.plot)

plotCommAxes(TSharedStreng1.plot, TSharedStreng2.plot, "Strength of Identification", "Proportion of Traits in Common")

Does positvity of identity predict asymmetries in valenced content?

Study 1

asym.pos1 <-lmer(pos ~ pndiff + ( pndiff | subID) + (1 | id), data=idShort1)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0303214 (tol = 0.002, component 1)
summary(asym.pos1)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: pos ~ pndiff + (pndiff | subID) + (1 | id)
##    Data: idShort1
## 
## REML criterion at convergence: 6431
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.5569 -0.5576  0.1762  0.6505  2.7125 
## 
## Random effects:
##  Groups   Name        Variance  Std.Dev. Corr 
##  subID    (Intercept) 0.4498030 0.67067       
##           pndiff      0.0001272 0.01128  -0.73
##  id       (Intercept) 0.3195272 0.56527       
##  Residual             1.2806356 1.13165       
## Number of obs: 1968, groups:  subID, 246; id, 8
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)  5.609290   0.206725  7.801213  27.134 5.28e-09 ***
## pndiff       0.014967   0.001928 66.044743   7.765 6.96e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##        (Intr)
## pndiff -0.131
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.0303214 (tol = 0.002, component 1)

Study 2

asym.pos2 <-lmer(pos ~ pndiff + ( pndiff | subID) + (1 | id), data=idShort2)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.651478 (tol = 0.002, component 1)
summary(asym.pos2)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: pos ~ pndiff + (pndiff | subID) + (1 | id)
##    Data: idShort2
## 
## REML criterion at convergence: 6368.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.3048 -0.5398  0.1799  0.6726  2.1612 
## 
## Random effects:
##  Groups   Name        Variance  Std.Dev. Corr 
##  subID    (Intercept) 0.3769815 0.61399       
##           pndiff      0.0001139 0.01067  -0.68
##  id       (Intercept) 0.3259019 0.57088       
##  Residual             1.2396391 1.11339       
## Number of obs: 1976, groups:  subID, 247; id, 8
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)  5.700323   0.207770  5.291264  27.436 6.55e-07 ***
## pndiff       0.013335   0.002208 28.543767   6.039 1.52e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##        (Intr)
## pndiff -0.112
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.651478 (tol = 0.002, component 1)

More trait overlap predicts stronger group identification

Study 1

tcomm.streng1 <-lmer(scale(streng) ~ scale(traitCommNod) + ( scale(traitCommNod) | subID) + (1 | id), data=idShort1)
summary(tcomm.streng1)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(streng) ~ scale(traitCommNod) + (scale(traitCommNod) |  
##     subID) + (1 | id)
##    Data: idShort1
## 
## REML criterion at convergence: 5129.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.5840 -0.3318  0.1614  0.6371  2.3258 
## 
## Random effects:
##  Groups   Name                Variance Std.Dev. Corr 
##  subID    (Intercept)         0.17637  0.4200        
##           scale(traitCommNod) 0.01621  0.1273   -0.10
##  id       (Intercept)         0.15736  0.3967        
##  Residual                     0.66902  0.8179        
## Number of obs: 1968, groups:  subID, 246; id, 8
## 
## Fixed effects:
##                       Estimate Std. Error         df t value Pr(>|t|)   
## (Intercept)         -0.0004936  0.1441315  7.5407501  -0.003  0.99736   
## scale(traitCommNod)  0.0919415  0.0296902 53.5611864   3.097  0.00311 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## scl(trtCmN) 0.001

Study 2

tcomm.streng2 <-lmer(scale(streng) ~ scale(traitCommNod) + ( scale(traitCommNod) | subID) + (1 | id), data=idShort2)
## boundary (singular) fit: see help('isSingular')
summary(tcomm.streng2)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(streng) ~ scale(traitCommNod) + (scale(traitCommNod) |  
##     subID) + (1 | id)
##    Data: idShort2
## 
## REML criterion at convergence: 5026.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.5692 -0.3732  0.1179  0.6107  2.6993 
## 
## Random effects:
##  Groups   Name                Variance  Std.Dev. Corr 
##  subID    (Intercept)         0.2213026 0.47043       
##           scale(traitCommNod) 0.0002155 0.01468  -1.00
##  id       (Intercept)         0.1876325 0.43317       
##  Residual                     0.6170142 0.78550       
## Number of obs: 1976, groups:  subID, 247; id, 8
## 
## Fixed effects:
##                       Estimate Std. Error         df t value Pr(>|t|)  
## (Intercept)          -0.001446   0.157044   7.537281  -0.009   0.9929  
## scale(traitCommNod)   0.064150   0.027591 326.148512   2.325   0.0207 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## scl(trtCmN) -0.010
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')

More identity overlap predicts stronger group identification

Study 1

icomm.streng1 <-lmer(scale(streng) ~ scale(idCommNod) + ( scale(idCommNod) | subID) + (1 | id), data=idShort1)
summary(icomm.streng1)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(streng) ~ scale(idCommNod) + (scale(idCommNod) | subID) +  
##     (1 | id)
##    Data: idShort1
## 
## REML criterion at convergence: 5134.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.6705 -0.3384  0.1697  0.6257  2.2069 
## 
## Random effects:
##  Groups   Name             Variance Std.Dev. Corr
##  subID    (Intercept)      0.16657  0.4081       
##           scale(idCommNod) 0.02676  0.1636   0.23
##  id       (Intercept)      0.16302  0.4038       
##  Residual                  0.67173  0.8196       
## Number of obs: 1968, groups:  subID, 246; id, 8
## 
## Fixed effects:
##                  Estimate Std. Error       df t value Pr(>|t|)  
## (Intercept)       0.00205    0.14655  7.51862   0.014   0.9892  
## scale(idCommNod)  0.05597    0.03220 98.60471   1.738   0.0853 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## scl(dCmmNd) 0.032

Study 2

icomm.streng2 <-lmer(scale(streng) ~ scale(idCommNod) + ( scale(idCommNod) | subID) + (1 | id), data=idShort2)
summary(icomm.streng2)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(streng) ~ scale(idCommNod) + (scale(idCommNod) | subID) +  
##     (1 | id)
##    Data: idShort2
## 
## REML criterion at convergence: 5025.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.6236 -0.3644  0.1267  0.6091  2.2873 
## 
## Random effects:
##  Groups   Name             Variance Std.Dev. Corr
##  subID    (Intercept)      0.19083  0.4368       
##           scale(idCommNod) 0.02119  0.1456   0.39
##  id       (Intercept)      0.18922  0.4350       
##  Residual                  0.61844  0.7864       
## Number of obs: 1976, groups:  subID, 247; id, 8
## 
## Fixed effects:
##                    Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)        0.002475   0.157462   7.491891   0.016    0.988
## scale(idCommNod)  -0.023603   0.030195 157.227342  -0.782    0.436
## 
## Correlation of Fixed Effects:
##             (Intr)
## scl(dCmmNd) 0.038

Individual Differences Predict Similarity in Identity Judgments

m <-lmer(scale(posDist) ~ scale(SE) + ( scale(SE) | subID), data=idSim1)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0296702 (tol = 0.002, component 1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(posDist) ~ scale(SE) + (scale(SE) | subID)
##    Data: idSim1
## 
## REML criterion at convergence: 18732.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.2789 -0.7439 -0.1736  0.6267  3.7725 
## 
## Random effects:
##  Groups   Name        Variance  Std.Dev. Corr
##  subID    (Intercept) 0.1468940 0.38327      
##           scale(SE)   0.0002704 0.01644  0.82
##  Residual             0.8421904 0.91771      
## Number of obs: 6860, groups:  subID, 245
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)  -0.00247    0.02690 242.21879  -0.092   0.9269    
## scale(SE)    -0.09315    0.02695 160.08071  -3.457   0.0007 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##           (Intr)
## scale(SE) 0.059 
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.0296702 (tol = 0.002, component 1)
m <-lmer(scale(strengDist) ~ scale(NFC) + ( scale(NFC) | subID), data=idSim1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(strengDist) ~ scale(NFC) + (scale(NFC) | subID)
##    Data: idSim1
## 
## REML criterion at convergence: 18474
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.6773 -0.6982 -0.1291  0.6265  4.4272 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr
##  subID    (Intercept) 0.16739  0.4091       
##           scale(NFC)  0.03351  0.1831   0.05
##  Residual             0.79366  0.8909       
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##               Estimate Std. Error         df t value Pr(>|t|)   
## (Intercept)   0.006492   0.030100 239.301125   0.216  0.82943   
## scale(NFC)   -0.100677   0.033420 117.159814  -3.012  0.00318 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr)
## scale(NFC) 0.007
m <-lmer(scale(posDist) ~ scale(DS) + ( 1 | subID), data=idSim1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(posDist) ~ scale(DS) + (1 | subID)
##    Data: idSim1
## 
## REML criterion at convergence: 18835.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3055 -0.7358 -0.1728  0.6372  3.7564 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.150    0.3873  
##  Residual             0.845    0.9192  
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##               Estimate Std. Error         df t value Pr(>|t|)   
## (Intercept) -8.091e-15  2.706e-02  2.440e+02   0.000  1.00000   
## scale(DS)    7.973e-02  2.707e-02  2.440e+02   2.946  0.00353 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##           (Intr)
## scale(DS) 0.000
m <-lmer(scale(posDist) ~ scale(SCC) + ( 1 | subID), data=idSim1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(posDist) ~ scale(SCC) + (1 | subID)
##    Data: idSim1
## 
## REML criterion at convergence: 18839.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.2954 -0.7458 -0.1698  0.6326  3.7620 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.1532   0.3914  
##  Residual             0.8450   0.9192  
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##               Estimate Std. Error         df t value Pr(>|t|)  
## (Intercept) -8.551e-15  2.730e-02  2.440e+02   0.000   1.0000  
## scale(SCC)  -5.634e-02  2.731e-02  2.440e+02  -2.063   0.0401 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr)
## scale(SCC) 0.000
m <-lmer(scale(posDist) ~ scale(MemSE) + ( 1 | subID), data=idSim1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(posDist) ~ scale(MemSE) + (1 | subID)
##    Data: idSim1
## 
## REML criterion at convergence: 18836.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.2769 -0.7369 -0.1764  0.6291  3.7572 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.1508   0.3884  
##  Residual             0.8450   0.9192  
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##                Estimate Std. Error         df t value Pr(>|t|)   
## (Intercept)  -8.325e-15  2.713e-02  2.440e+02   0.000  1.00000   
## scale(MemSE) -7.438e-02  2.713e-02  2.440e+02  -2.742  0.00656 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## scale(MmSE) 0.000
m <-lmer(scale(posDist) ~ scale(PrivCSE) + ( 1 | subID), data=idSim1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(posDist) ~ scale(PrivCSE) + (1 | subID)
##    Data: idSim1
## 
## REML criterion at convergence: 18840.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.2879 -0.7390 -0.1779  0.6307  3.7496 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.1541   0.3926  
##  Residual             0.8450   0.9192  
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)  
## (Intercept)    -8.349e-15  2.737e-02  2.440e+02    0.00   1.0000  
## scale(PrivCSE) -4.790e-02  2.737e-02  2.440e+02   -1.75   0.0814 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## scl(PrvCSE) 0.000
m <-lmer(scale(posDist) ~ scale(PubCSE) + ( 1 | subID), data=idSim1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(posDist) ~ scale(PubCSE) + (1 | subID)
##    Data: idSim1
## 
## REML criterion at convergence: 18838.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.2988 -0.7332 -0.1769  0.6322  3.7589 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.1525   0.3906  
##  Residual             0.8450   0.9192  
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##                 Estimate Std. Error         df t value Pr(>|t|)  
## (Intercept)   -7.974e-15  2.725e-02  2.440e+02   0.000    1.000  
## scale(PubCSE) -6.192e-02  2.726e-02  2.440e+02  -2.272    0.024 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## scal(PbCSE) 0.000
m <-lmer(scale(posDist) ~ scale(MemSE) + ( 1 | subID), data=idSim1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(posDist) ~ scale(MemSE) + (1 | subID)
##    Data: idSim1
## 
## REML criterion at convergence: 18836.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.2769 -0.7369 -0.1764  0.6291  3.7572 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.1508   0.3884  
##  Residual             0.8450   0.9192  
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##                Estimate Std. Error         df t value Pr(>|t|)   
## (Intercept)  -8.325e-15  2.713e-02  2.440e+02   0.000  1.00000   
## scale(MemSE) -7.438e-02  2.713e-02  2.440e+02  -2.742  0.00656 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## scale(MmSE) 0.000

Individual Differences Predict Pairwise Overlap in Traits in Common

Study 1

Positive

m <-lmer(scale(idtpSim) ~ scale(SCC) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(SCC) + scale(idtnSim) + (scale(idtnSim) |  
##     subID)
##    Data: idSim1
## 
## REML criterion at convergence: 16716.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.4786 -0.6413 -0.1864  0.5161  7.0170 
## 
## Random effects:
##  Groups   Name           Variance  Std.Dev. Corr
##  subID    (Intercept)    0.4005422 0.63288      
##           scale(idtnSim) 0.0006437 0.02537  1.00
##  Residual                0.5947587 0.77121      
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)    -4.423e-03  4.141e-02  2.426e+02  -0.107    0.915
## scale(SCC)      4.557e-02  4.092e-02  2.360e+02   1.114    0.267
## scale(idtnSim)  2.107e-02  1.298e-02  3.082e+03   1.623    0.105
## 
## Correlation of Fixed Effects:
##             (Intr) s(SCC)
## scale(SCC)  0.000        
## scal(dtnSm) 0.127  0.003 
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
m <-lmer(scale(idtpSim) ~ scale(Ind) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(Ind) + scale(idtnSim) + (scale(idtnSim) |  
##     subID)
##    Data: idSim1
## 
## REML criterion at convergence: 16702
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.4908 -0.6446 -0.1879  0.5126  7.0203 
## 
## Random effects:
##  Groups   Name           Variance  Std.Dev. Corr
##  subID    (Intercept)    0.3751958 0.61253      
##           scale(idtnSim) 0.0005848 0.02418  1.00
##  Residual                0.5948167 0.77124      
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)      -0.00438    0.04015  242.33298  -0.109   0.9132    
## scale(Ind)        0.16080    0.03989  240.59501   4.031 7.44e-05 ***
## scale(idtnSim)    0.02187    0.01295 3209.66171   1.689   0.0914 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(I)
## scale(Ind)  0.000        
## scal(dtnSm) 0.122  0.003 
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
m <-lmer(scale(idtpSim) ~ scale(Inter) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(Inter) + scale(idtnSim) + (scale(idtnSim) |  
##     subID)
##    Data: idSim1
## 
## REML criterion at convergence: 16711.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.4824 -0.6431 -0.1881  0.5151  7.0156 
## 
## Random effects:
##  Groups   Name           Variance  Std.Dev. Corr
##  subID    (Intercept)    0.3927089 0.62666      
##           scale(idtnSim) 0.0006496 0.02549  1.00
##  Residual                0.5947618 0.77121      
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)  
## (Intercept)    -4.357e-03  4.102e-02  2.426e+02  -0.106   0.9155  
## scale(Inter)    9.836e-02  4.069e-02  2.402e+02   2.417   0.0164 *
## scale(idtnSim)  2.070e-02  1.298e-02  3.035e+03   1.595   0.1107  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(I)
## scale(Intr)  0.001       
## scal(dtnSm)  0.128 -0.017
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
m <-lmer(scale(idtpSim) ~ scale(SWLS) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(SWLS) + scale(idtnSim) + (scale(idtnSim) |  
##     subID)
##    Data: idSim1
## 
## REML criterion at convergence: 16711.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.4797 -0.6423 -0.1871  0.5160  7.0191 
## 
## Random effects:
##  Groups   Name           Variance  Std.Dev. Corr
##  subID    (Intercept)    0.3916795 0.62584      
##           scale(idtnSim) 0.0005174 0.02275  1.00
##  Residual                0.5948193 0.77125      
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)  
## (Intercept)    -3.968e-03  4.097e-02  2.425e+02  -0.097   0.9229  
## scale(SWLS)     9.839e-02  4.068e-02  2.400e+02   2.419   0.0163 *
## scale(idtnSim)  2.099e-02  1.295e-02  3.381e+03   1.620   0.1052  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) s(SWLS
## scale(SWLS)  0.000       
## scal(dtnSm)  0.114 -0.005
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
m <-lmer(scale(idtpSim) ~ scale(IdImp) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(IdImp) + scale(idtnSim) + (scale(idtnSim) |  
##     subID)
##    Data: idSim1
## 
## REML criterion at convergence: 16717.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.4736 -0.6428 -0.1864  0.5163  7.0170 
## 
## Random effects:
##  Groups   Name           Variance  Std.Dev. Corr
##  subID    (Intercept)    0.4017482 0.6338       
##           scale(idtnSim) 0.0005903 0.0243   1.00
##  Residual                0.5947830 0.7712       
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)    -4.186e-03  4.147e-02  2.426e+02  -0.101    0.920
## scale(IdImp)    2.163e-02  4.120e-02  2.416e+02   0.525    0.600
## scale(idtnSim)  2.079e-02  1.297e-02  3.183e+03   1.603    0.109
## 
## Correlation of Fixed Effects:
##             (Intr) sc(II)
## scal(IdImp)  0.002       
## scal(dtnSm)  0.122 -0.021
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
m <-lmer(scale(idtpSim) ~ scale(phi) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(phi) + scale(idtnSim) + (scale(idtnSim) |  
##     subID)
##    Data: idSim1
## 
## REML criterion at convergence: 16710.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.4715 -0.6405 -0.1925  0.5159  7.0232 
## 
## Random effects:
##  Groups   Name           Variance  Std.Dev. Corr
##  subID    (Intercept)    0.3906973 0.6251       
##           scale(idtnSim) 0.0006454 0.0254   1.00
##  Residual                0.5947354 0.7712       
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)   
## (Intercept)    -4.231e-03  4.092e-02  2.427e+02  -0.103  0.91775   
## scale(phi)     -1.100e-01  4.055e-02  2.399e+02  -2.712  0.00716 **
## scale(idtnSim)  2.071e-02  1.297e-02  3.076e+03   1.596  0.11060   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(p)
## scale(phi)  -0.002       
## scal(dtnSm)  0.127  0.017
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
m <-lmer(scale(idtpSim) ~ scale(overlap_norm) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## scale(idtpSim) ~ scale(overlap_norm) + scale(idtnSim) + (scale(idtnSim) |  
##     subID)
##    Data: idSim1
## 
## REML criterion at convergence: 16239.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.6903 -0.6401 -0.2083  0.5215  7.0918 
## 
## Random effects:
##  Groups   Name           Variance  Std.Dev. Corr
##  subID    (Intercept)    3.778e-02 0.194382     
##           scale(idtnSim) 3.785e-06 0.001946 1.00
##  Residual                5.950e-01 0.771349     
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##                       Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)         -6.631e-05  1.549e-02  2.416e+02  -0.004    0.997    
## scale(overlap_norm)  6.028e-01  1.563e-02  2.477e+02  38.560   <2e-16 ***
## scale(idtnSim)       1.526e-02  1.140e-02  2.282e+03   1.339    0.181    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(_)
## scl(vrlp_n)  0.001       
## scal(dtnSm)  0.011 -0.134
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
m <-lmer(scale(idtpSim) ~ scale(H_index) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(H_index) + scale(idtnSim) + (scale(idtnSim) |  
##     subID)
##    Data: idSim1
## 
## REML criterion at convergence: 16487.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2396 -0.6200 -0.1983  0.5115  7.0483 
## 
## Random effects:
##  Groups   Name           Variance  Std.Dev. Corr
##  subID    (Intercept)    0.1443075 0.37988      
##           scale(idtnSim) 0.0003823 0.01955  1.00
##  Residual                0.5946927 0.77116      
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)    -2.704e-04  2.595e-02  2.435e+02  -0.010    0.992    
## scale(H_index)  5.087e-01  2.611e-02  2.524e+02  19.484   <2e-16 ***
## scale(idtnSim)  1.038e-02  1.258e-02  2.877e+03   0.825    0.410    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) sc(H_)
## scal(H_ndx)  0.009       
## scal(dtnSm)  0.102 -0.109
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
m <-lmer(scale(idtpSim) ~ scale(SE) + scale(idtnSim) + ( scale(idtnSim)| subID), data=idSim1)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(SE) + scale(idtnSim) + (scale(idtnSim) |  
##     subID)
##    Data: idSim1
## 
## REML criterion at convergence: 16640.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.4824 -0.6459 -0.1823  0.5173  7.0192 
## 
## Random effects:
##  Groups   Name           Variance  Std.Dev. Corr
##  subID    (Intercept)    0.3947209 0.62827      
##           scale(idtnSim) 0.0005584 0.02363  1.00
##  Residual                0.5943377 0.77093      
## Number of obs: 6860, groups:  subID, 245
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)  
## (Intercept)    -1.734e-03  4.120e-02  2.415e+02  -0.042   0.9665  
## scale(SE)       8.615e-02  4.073e-02  2.340e+02   2.115   0.0355 *
## scale(idtnSim)  2.233e-02  1.299e-02  3.250e+03   1.719   0.0857 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) sc(SE)
## scale(SE)   -0.001       
## scal(dtnSm)  0.119  0.010
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
m <-lmer(scale(idtpSim) ~ scale(NFC) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(NFC) + scale(idtnSim) + (scale(idtnSim) |  
##     subID)
##    Data: idSim1
## 
## REML criterion at convergence: 16707
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.4868 -0.6414 -0.1872  0.5156  7.0076 
## 
## Random effects:
##  Groups   Name           Variance  Std.Dev. Corr
##  subID    (Intercept)    0.3848073 0.62033      
##           scale(idtnSim) 0.0006092 0.02468  1.00
##  Residual                0.5947457 0.77120      
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)   
## (Intercept)    -4.199e-03  4.063e-02  2.426e+02  -0.103  0.91776   
## scale(NFC)      1.321e-01  4.006e-02  2.335e+02   3.298  0.00112 **
## scale(idtnSim)  2.109e-02  1.296e-02  3.179e+03   1.627  0.10376   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) s(NFC)
## scale(NFC)   0.001       
## scal(dtnSm)  0.124 -0.008
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
m <-lmer(scale(idtpSim) ~ scale(DS) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(DS) + scale(idtnSim) + (scale(idtnSim) |  
##     subID)
##    Data: idSim1
## 
## REML criterion at convergence: 16714.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.4825 -0.6426 -0.1918  0.5151  7.0165 
## 
## Random effects:
##  Groups   Name           Variance  Std.Dev. Corr
##  subID    (Intercept)    0.3978320 0.63074      
##           scale(idtnSim) 0.0006567 0.02563  1.00
##  Residual                0.5947443 0.77120      
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)  
## (Intercept)    -4.369e-03  4.128e-02  2.426e+02  -0.106   0.9158  
## scale(DS)      -7.053e-02  4.080e-02  2.368e+02  -1.728   0.0852 .
## scale(idtnSim)  2.086e-02  1.298e-02  3.053e+03   1.607   0.1081  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) sc(DS)
## scale(DS)   -0.001       
## scal(dtnSm)  0.128  0.011
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
m <-lmer(scale(idtpSim) ~ scale(SCC) + scale(idtnSim) + ( scale(idtnSim)| subID), data=idSim1)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(SCC) + scale(idtnSim) + (scale(idtnSim) |  
##     subID)
##    Data: idSim1
## 
## REML criterion at convergence: 16716.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.4786 -0.6413 -0.1864  0.5161  7.0170 
## 
## Random effects:
##  Groups   Name           Variance  Std.Dev. Corr
##  subID    (Intercept)    0.4005422 0.63288      
##           scale(idtnSim) 0.0006437 0.02537  1.00
##  Residual                0.5947587 0.77121      
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)    -4.423e-03  4.141e-02  2.426e+02  -0.107    0.915
## scale(SCC)      4.557e-02  4.092e-02  2.360e+02   1.114    0.267
## scale(idtnSim)  2.107e-02  1.298e-02  3.082e+03   1.623    0.105
## 
## Correlation of Fixed Effects:
##             (Intr) s(SCC)
## scale(SCC)  0.000        
## scal(dtnSm) 0.127  0.003 
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
m <-lmer(scale(idtpSim) ~ scale(MemSE) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(MemSE) + scale(idtnSim) + (scale(idtnSim) |  
##     subID)
##    Data: idSim1
## 
## REML criterion at convergence: 16715.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.4749 -0.6385 -0.1888  0.5180  7.0187 
## 
## Random effects:
##  Groups   Name           Variance  Std.Dev. Corr
##  subID    (Intercept)    0.3986332 0.63137      
##           scale(idtnSim) 0.0006661 0.02581  1.00
##  Residual                0.5947628 0.77121      
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)    -4.488e-03  4.131e-02  2.426e+02  -0.109    0.914
## scale(MemSE)    6.233e-02  4.100e-02  2.410e+02   1.520    0.130
## scale(idtnSim)  2.111e-02  1.298e-02  3.042e+03   1.626    0.104
## 
## Correlation of Fixed Effects:
##             (Intr) s(MSE)
## scale(MmSE) 0.000        
## scal(dtnSm) 0.129  0.001 
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
m <-lmer(scale(idtpSim) ~ scale(PrivCSE) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(PrivCSE) + scale(idtnSim) + (scale(idtnSim) |  
##     subID)
##    Data: idSim1
## 
## REML criterion at convergence: 16714.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.4682 -0.6374 -0.1861  0.5192  7.0167 
## 
## Random effects:
##  Groups   Name           Variance  Std.Dev. Corr
##  subID    (Intercept)    0.3965386 0.62971      
##           scale(idtnSim) 0.0006229 0.02496  1.00
##  Residual                0.5947770 0.77122      
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)  
## (Intercept)    -4.242e-03  4.121e-02  2.426e+02  -0.103   0.9181  
## scale(PrivCSE)  7.509e-02  4.082e-02  2.385e+02   1.839   0.0671 .
## scale(idtnSim)  2.097e-02  1.297e-02  3.153e+03   1.616   0.1061  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) s(PCSE
## scl(PrvCSE)  0.002       
## scal(dtnSm)  0.125 -0.006
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
m <-lmer(scale(idtpSim) ~ scale(PubCSE) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(PubCSE) + scale(idtnSim) + (scale(idtnSim) |  
##     subID)
##    Data: idSim1
## 
## REML criterion at convergence: 16710.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.4773 -0.6405 -0.1872  0.5150  7.0088 
## 
## Random effects:
##  Groups   Name           Variance  Std.Dev. Corr
##  subID    (Intercept)    0.3910845 0.62537      
##           scale(idtnSim) 0.0007795 0.02792  1.00
##  Residual                0.5947460 0.77120      
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)   
## (Intercept)      -0.00490    0.04094  242.49877  -0.120  0.90483   
## scale(PubCSE)     0.10767    0.04056  239.44539   2.655  0.00847 **
## scale(idtnSim)    0.02139    0.01300 2771.23729   1.646  0.09984 . 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) s(PCSE
## scal(PbCSE)  0.000       
## scal(dtnSm)  0.140 -0.001
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
m <-lmer(scale(idtpSim) ~ scale(MemSE) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(MemSE) + scale(idtnSim) + (scale(idtnSim) |  
##     subID)
##    Data: idSim1
## 
## REML criterion at convergence: 16715.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.4749 -0.6385 -0.1888  0.5180  7.0187 
## 
## Random effects:
##  Groups   Name           Variance  Std.Dev. Corr
##  subID    (Intercept)    0.3986332 0.63137      
##           scale(idtnSim) 0.0006661 0.02581  1.00
##  Residual                0.5947628 0.77121      
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)    -4.488e-03  4.131e-02  2.426e+02  -0.109    0.914
## scale(MemSE)    6.233e-02  4.100e-02  2.410e+02   1.520    0.130
## scale(idtnSim)  2.111e-02  1.298e-02  3.042e+03   1.626    0.104
## 
## Correlation of Fixed Effects:
##             (Intr) s(MSE)
## scale(MmSE) 0.000        
## scal(dtnSm) 0.129  0.001 
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')

Negative

m <-lmer(scale(idtnSim) ~ scale(SCC) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(SCC) + scale(idtpSim) + (scale(idtpSim) |  
##     subID)
##    Data: idSim1
## 
## REML criterion at convergence: 15813.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2650 -0.5990 -0.1798  0.3468  4.2583 
## 
## Random effects:
##  Groups   Name           Variance  Std.Dev. Corr
##  subID    (Intercept)    4.840e-01 0.695729     
##           scale(idtpSim) 7.144e-06 0.002673 1.00
##  Residual                5.158e-01 0.718221     
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)    -4.549e-04  4.519e-02  2.426e+02  -0.010    0.992
## scale(SCC)     -1.486e-03  4.521e-02  2.428e+02  -0.033    0.974
## scale(idtpSim)  1.454e-02  1.129e-02  5.455e+03   1.288    0.198
## 
## Correlation of Fixed Effects:
##             (Intr) s(SCC)
## scale(SCC)   0.000       
## scal(dtpSm)  0.015 -0.010
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
m <-lmer(scale(idtnSim) ~ scale(Ind) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(Ind) + scale(idtpSim) + (scale(idtpSim) |  
##     subID)
##    Data: idSim1
## 
## REML criterion at convergence: 15813.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2641 -0.5982 -0.1793  0.3462  4.2579 
## 
## Random effects:
##  Groups   Name           Variance  Std.Dev. Corr
##  subID    (Intercept)    4.838e-01 0.695593     
##           scale(idtpSim) 7.559e-06 0.002749 1.00
##  Residual                5.158e-01 0.718221     
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)    -4.755e-04  4.519e-02  2.425e+02  -0.011    0.992
## scale(Ind)     -1.296e-02  4.523e-02  2.433e+02  -0.286    0.775
## scale(idtpSim)  1.466e-02  1.129e-02  5.536e+03   1.298    0.194
## 
## Correlation of Fixed Effects:
##             (Intr) scl(I)
## scale(Ind)   0.001       
## scal(dtpSm)  0.016 -0.039
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
m <-lmer(scale(idtnSim) ~ scale(Inter) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00533876 (tol = 0.002, component 1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(Inter) + scale(idtpSim) + (scale(idtpSim) |  
##     subID)
##    Data: idSim1
## 
## REML criterion at convergence: 15813.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2665 -0.5997 -0.1810  0.3469  4.2535 
## 
## Random effects:
##  Groups   Name           Variance  Std.Dev. Corr
##  subID    (Intercept)    4.830e-01 0.694979     
##           scale(idtpSim) 7.996e-06 0.002828 1.00
##  Residual                5.158e-01 0.718217     
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)    -4.681e-04  4.515e-02  2.426e+02  -0.010    0.992
## scale(Inter)    3.424e-02  4.515e-02  2.420e+02   0.758    0.449
## scale(idtpSim)  1.436e-02  1.129e-02  5.503e+03   1.272    0.204
## 
## Correlation of Fixed Effects:
##             (Intr) scl(I)
## scale(Intr)  0.000       
## scal(dtpSm)  0.016 -0.023
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00533876 (tol = 0.002, component 1)
m <-lmer(scale(idtnSim) ~ scale(SWLS) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(SWLS) + scale(idtpSim) + (scale(idtpSim) |  
##     subID)
##    Data: idSim1
## 
## REML criterion at convergence: 15813.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2654 -0.5992 -0.1798  0.3471  4.2585 
## 
## Random effects:
##  Groups   Name           Variance  Std.Dev. Corr
##  subID    (Intercept)    4.840e-01 0.695676     
##           scale(idtpSim) 6.354e-06 0.002521 1.00
##  Residual                5.158e-01 0.718219     
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)    -4.249e-04  4.519e-02  2.426e+02  -0.009    0.993
## scale(SWLS)     1.098e-02  4.522e-02  2.431e+02   0.243    0.808
## scale(idtpSim)  1.446e-02  1.129e-02  5.473e+03   1.281    0.200
## 
## Correlation of Fixed Effects:
##             (Intr) s(SWLS
## scale(SWLS)  0.000       
## scal(dtpSm)  0.014 -0.026
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
m <-lmer(scale(idtnSim) ~ scale(IdImp) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(IdImp) + scale(idtpSim) + (scale(idtpSim) |  
##     subID)
##    Data: idSim1
## 
## REML criterion at convergence: 15812
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2633 -0.5996 -0.1774  0.3456  4.2555 
## 
## Random effects:
##  Groups   Name           Variance  Std.Dev. Corr
##  subID    (Intercept)    4.803e-01 0.693052     
##           scale(idtpSim) 3.257e-06 0.001805 1.00
##  Residual                5.158e-01 0.718217     
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)    -3.034e-04  4.503e-02  2.426e+02  -0.007    0.995
## scale(IdImp)    6.210e-02  4.505e-02  2.431e+02   1.378    0.169
## scale(idtpSim)  1.448e-02  1.128e-02  5.581e+03   1.284    0.199
## 
## Correlation of Fixed Effects:
##             (Intr) sc(II)
## scal(IdImp)  0.000       
## scal(dtpSm)  0.010 -0.007
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
m <-lmer(scale(idtnSim) ~ scale(phi) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(phi) + scale(idtpSim) + (scale(idtpSim) |  
##     subID)
##    Data: idSim1
## 
## REML criterion at convergence: 15812.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2667 -0.6012 -0.1806  0.3483  4.2557 
## 
## Random effects:
##  Groups   Name           Variance  Std.Dev. Corr
##  subID    (Intercept)    4.807e-01 0.693293     
##           scale(idtpSim) 7.805e-06 0.002794 1.00
##  Residual                5.158e-01 0.718218     
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)    -4.520e-04  4.504e-02  2.427e+02  -0.010    0.992
## scale(phi)     -5.882e-02  4.504e-02  2.415e+02  -1.306    0.193
## scale(idtpSim)  1.416e-02  1.129e-02  5.355e+03   1.254    0.210
## 
## Correlation of Fixed Effects:
##             (Intr) scl(p)
## scale(phi)  0.000        
## scal(dtpSm) 0.016  0.029 
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
m <-lmer(scale(idtnSim) ~ scale(overlap_norm) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## scale(idtnSim) ~ scale(overlap_norm) + scale(idtpSim) + (scale(idtpSim) |  
##     subID)
##    Data: idSim1
## 
## REML criterion at convergence: 15798
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2469 -0.6015 -0.1822  0.3457  4.2502 
## 
## Random effects:
##  Groups   Name           Variance  Std.Dev.  Corr
##  subID    (Intercept)    4.536e-01 0.6735281     
##           scale(idtpSim) 1.252e-07 0.0003538 1.00
##  Residual                5.158e-01 0.7181879     
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##                       Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)         -8.580e-06  4.381e-02  2.431e+02   0.000     1.00    
## scale(overlap_norm)  1.795e-01  4.436e-02  2.540e+02   4.047  6.9e-05 ***
## scale(idtpSim)       7.880e-03  1.140e-02  6.691e+03   0.691     0.49    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(_)
## scl(vrlp_n)  0.000       
## scal(dtpSm)  0.002 -0.155
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
m <-lmer(scale(idtnSim) ~ scale(H_index) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(H_index) + scale(idtpSim) + (scale(idtpSim) |  
##     subID)
##    Data: idSim1
## 
## REML criterion at convergence: 15788.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2425 -0.6031 -0.1772  0.3409  4.2547 
## 
## Random effects:
##  Groups   Name           Variance  Std.Dev. Corr
##  subID    (Intercept)    4.348e-01 0.659367     
##           scale(idtpSim) 1.657e-06 0.001287 1.00
##  Residual                5.158e-01 0.718192     
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)    -1.969e-05  4.292e-02  2.433e+02   0.000    1.000    
## scale(H_index)  2.256e-01  4.335e-02  2.516e+02   5.204 4.06e-07 ***
## scale(idtpSim)  7.461e-03  1.136e-02  5.881e+03   0.657    0.512    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) sc(H_)
## scal(H_ndx)  0.001       
## scal(dtpSm)  0.007 -0.134
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
m <-lmer(scale(idtnSim) ~ scale(SE) + scale(idtpSim) + ( scale(idtpSim)| subID), data=idSim1)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(SE) + scale(idtpSim) + (scale(idtpSim) |  
##     subID)
##    Data: idSim1
## 
## REML criterion at convergence: 15741.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2671 -0.5978 -0.1800  0.3433  4.2607 
## 
## Random effects:
##  Groups   Name           Variance  Std.Dev. Corr
##  subID    (Intercept)    4.854e-01 0.696722     
##           scale(idtpSim) 8.092e-06 0.002845 1.00
##  Residual                5.151e-01 0.717728     
## Number of obs: 6860, groups:  subID, 245
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)    -1.168e-03  4.535e-02  2.415e+02  -0.026    0.979
## scale(SE)      -1.841e-02  4.537e-02  2.419e+02  -0.406    0.685
## scale(idtpSim)  1.545e-02  1.131e-02  5.395e+03   1.367    0.172
## 
## Correlation of Fixed Effects:
##             (Intr) sc(SE)
## scale(SE)    0.000       
## scal(dtpSm)  0.016 -0.022
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
m <-lmer(scale(idtnSim) ~ scale(NFC) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(NFC) + scale(idtpSim) + (scale(idtpSim) |  
##     subID)
##    Data: idSim1
## 
## REML criterion at convergence: 15813.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2669 -0.6009 -0.1804  0.3468  4.2628 
## 
## Random effects:
##  Groups   Name           Variance  Std.Dev. Corr
##  subID    (Intercept)    4.829e-01 0.694940     
##           scale(idtpSim) 7.880e-06 0.002807 1.00
##  Residual                5.158e-01 0.718220     
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)    -4.610e-04  4.514e-02  2.427e+02  -0.010    0.992
## scale(NFC)      3.307e-02  4.519e-02  2.436e+02   0.732    0.465
## scale(idtpSim)  1.428e-02  1.129e-02  5.481e+03   1.265    0.206
## 
## Correlation of Fixed Effects:
##             (Intr) s(NFC)
## scale(NFC)   0.001       
## scal(dtpSm)  0.016 -0.033
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
m <-lmer(scale(idtnSim) ~ scale(DS) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(DS) + scale(idtpSim) + (scale(idtpSim) |  
##     subID)
##    Data: idSim1
## 
## REML criterion at convergence: 15813
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2709 -0.6015 -0.1804  0.3469  4.2619 
## 
## Random effects:
##  Groups   Name           Variance  Std.Dev. Corr
##  subID    (Intercept)    4.823e-01 0.69445      
##           scale(idtpSim) 9.120e-06 0.00302  1.00
##  Residual                5.158e-01 0.71822      
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)    -5.024e-04  4.511e-02  2.426e+02  -0.011    0.991
## scale(DS)      -4.233e-02  4.513e-02  2.430e+02  -0.938    0.349
## scale(idtpSim)  1.438e-02  1.129e-02  5.409e+03   1.274    0.203
## 
## Correlation of Fixed Effects:
##             (Intr) sc(DS)
## scale(DS)   0.000        
## scal(dtpSm) 0.017  0.017 
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
m <-lmer(scale(idtnSim) ~ scale(SCC) + scale(idtpSim) + ( scale(idtpSim)| subID), data=idSim1)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(SCC) + scale(idtpSim) + (scale(idtpSim) |  
##     subID)
##    Data: idSim1
## 
## REML criterion at convergence: 15813.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2650 -0.5990 -0.1798  0.3468  4.2583 
## 
## Random effects:
##  Groups   Name           Variance  Std.Dev. Corr
##  subID    (Intercept)    4.840e-01 0.695729     
##           scale(idtpSim) 7.144e-06 0.002673 1.00
##  Residual                5.158e-01 0.718221     
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)    -4.549e-04  4.519e-02  2.426e+02  -0.010    0.992
## scale(SCC)     -1.486e-03  4.521e-02  2.428e+02  -0.033    0.974
## scale(idtpSim)  1.454e-02  1.129e-02  5.455e+03   1.288    0.198
## 
## Correlation of Fixed Effects:
##             (Intr) s(SCC)
## scale(SCC)   0.000       
## scal(dtpSm)  0.015 -0.010
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
m <-lmer(scale(idtnSim) ~ scale(MemSE) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(MemSE) + scale(idtpSim) + (scale(idtpSim) |  
##     subID)
##    Data: idSim1
## 
## REML criterion at convergence: 15813.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2654 -0.5995 -0.1798  0.3463  4.2582 
## 
## Random effects:
##  Groups   Name           Variance  Std.Dev. Corr
##  subID    (Intercept)    4.840e-01 0.695711     
##           scale(idtpSim) 7.586e-06 0.002754 1.00
##  Residual                5.158e-01 0.718220     
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)    -4.672e-04  4.519e-02  2.426e+02  -0.010    0.992
## scale(MemSE)    5.847e-03  4.522e-02  2.431e+02   0.129    0.897
## scale(idtpSim)  1.451e-02  1.129e-02  5.440e+03   1.286    0.199
## 
## Correlation of Fixed Effects:
##             (Intr) s(MSE)
## scale(MmSE)  0.000       
## scal(dtpSm)  0.016 -0.014
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
m <-lmer(scale(idtnSim) ~ scale(PrivCSE) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(PrivCSE) + scale(idtpSim) + (scale(idtpSim) |  
##     subID)
##    Data: idSim1
## 
## REML criterion at convergence: 15812.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2680 -0.5992 -0.1808  0.3472  4.2532 
## 
## Random effects:
##  Groups   Name           Variance  Std.Dev. Corr
##  subID    (Intercept)    4.821e-01 0.694320     
##           scale(idtpSim) 7.391e-06 0.002719 1.00
##  Residual                5.158e-01 0.718219     
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)    -4.507e-04  4.511e-02  2.426e+02  -0.010    0.992
## scale(PrivCSE)  4.472e-02  4.511e-02  2.423e+02   0.991    0.322
## scale(idtpSim)  1.435e-02  1.129e-02  5.436e+03   1.272    0.204
## 
## Correlation of Fixed Effects:
##             (Intr) s(PCSE
## scl(PrvCSE)  0.000       
## scal(dtpSm)  0.015 -0.019
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
m <-lmer(scale(idtnSim) ~ scale(PubCSE) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00674211 (tol = 0.002, component 1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(PubCSE) + scale(idtpSim) + (scale(idtpSim) |  
##     subID)
##    Data: idSim1
## 
## REML criterion at convergence: 15813.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2653 -0.5991 -0.1797  0.3466  4.2582 
## 
## Random effects:
##  Groups   Name           Variance  Std.Dev. Corr
##  subID    (Intercept)    4.841e-01 0.695764     
##           scale(idtpSim) 7.341e-06 0.002709 1.00
##  Residual                5.158e-01 0.718219     
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)    -4.596e-04  4.520e-02  2.425e+02  -0.010    0.992
## scale(PubCSE)   1.305e-03  4.522e-02  2.430e+02   0.029    0.977
## scale(idtpSim)  1.453e-02  1.129e-02  5.471e+03   1.287    0.198
## 
## Correlation of Fixed Effects:
##             (Intr) s(PCSE
## scal(PbCSE)  0.000       
## scal(dtpSm)  0.015 -0.025
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00674211 (tol = 0.002, component 1)
m <-lmer(scale(idtnSim) ~ scale(MemSE) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(MemSE) + scale(idtpSim) + (scale(idtpSim) |  
##     subID)
##    Data: idSim1
## 
## REML criterion at convergence: 15813.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2654 -0.5995 -0.1798  0.3463  4.2582 
## 
## Random effects:
##  Groups   Name           Variance  Std.Dev. Corr
##  subID    (Intercept)    4.840e-01 0.695711     
##           scale(idtpSim) 7.586e-06 0.002754 1.00
##  Residual                5.158e-01 0.718220     
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)    -4.672e-04  4.519e-02  2.426e+02  -0.010    0.992
## scale(MemSE)    5.847e-03  4.522e-02  2.431e+02   0.129    0.897
## scale(idtpSim)  1.451e-02  1.129e-02  5.440e+03   1.286    0.199
## 
## Correlation of Fixed Effects:
##             (Intr) s(MSE)
## scale(MmSE)  0.000       
## scal(dtpSm)  0.016 -0.014
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')

Both

m <-lmer(scale(idtSim) ~ scale(SCC) + ( 1 | subID), data=idSim1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(SCC) + (1 | subID)
##    Data: idSim1
## 
## REML criterion at convergence: 16558.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2646 -0.6147 -0.1921  0.5184  7.7602 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.4219   0.6495  
##  Residual             0.5803   0.7618  
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##               Estimate Std. Error         df t value Pr(>|t|)
## (Intercept) -7.412e-15  4.242e-02  2.440e+02   0.000    1.000
## scale(SCC)   3.530e-02  4.242e-02  2.440e+02   0.832    0.406
## 
## Correlation of Fixed Effects:
##            (Intr)
## scale(SCC) 0.000
m <-lmer(scale(idtSim) ~ scale(Ind) + ( 1 | subID), data=idSim1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(Ind) + (1 | subID)
##    Data: idSim1
## 
## REML criterion at convergence: 16545.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2779 -0.6182 -0.1888  0.5172  7.7610 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.3977   0.6307  
##  Residual             0.5803   0.7618  
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##               Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept) -5.741e-15  4.124e-02  2.440e+02   0.000  1.00000    
## scale(Ind)   1.588e-01  4.125e-02  2.440e+02   3.851  0.00015 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr)
## scale(Ind) 0.000
m <-lmer(scale(idtSim) ~ scale(Inter) + ( 1 | subID), data=idSim1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(Inter) + (1 | subID)
##    Data: idSim1
## 
## REML criterion at convergence: 16554.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2693 -0.6133 -0.1927  0.5197  7.7622 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.4137   0.6432  
##  Residual             0.5803   0.7618  
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##                Estimate Std. Error         df t value Pr(>|t|)  
## (Intercept)  -5.445e-15  4.202e-02  2.440e+02   0.000   1.0000  
## scale(Inter)  9.676e-02  4.203e-02  2.440e+02   2.302   0.0222 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## scale(Intr) 0.000
m <-lmer(scale(idtSim) ~ scale(SWLS) + ( 1 | subID), data=idSim1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(SWLS) + (1 | subID)
##    Data: idSim1
## 
## REML criterion at convergence: 16555.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2661 -0.6148 -0.1935  0.5173  7.7605 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.4156   0.6447  
##  Residual             0.5803   0.7618  
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##               Estimate Std. Error         df t value Pr(>|t|)  
## (Intercept) -5.251e-15  4.212e-02  2.440e+02   0.000   1.0000  
## scale(SWLS)  8.645e-02  4.212e-02  2.440e+02   2.053   0.0412 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## scale(SWLS) 0.000
m <-lmer(scale(idtSim) ~ scale(IdImp) + ( 1 | subID), data=idSim1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(IdImp) + (1 | subID)
##    Data: idSim1
## 
## REML criterion at convergence: 16559.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2607 -0.6126 -0.1920  0.5190  7.7619 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.4228   0.6502  
##  Residual             0.5803   0.7618  
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##                Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)  -8.017e-15  4.246e-02  2.440e+02   0.000    1.000
## scale(IdImp)  1.843e-02  4.247e-02  2.440e+02   0.434    0.665
## 
## Correlation of Fixed Effects:
##             (Intr)
## scal(IdImp) 0.000
m <-lmer(scale(idtSim) ~ scale(phi) + ( 1 | subID), data=idSim1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(phi) + (1 | subID)
##    Data: idSim1
## 
## REML criterion at convergence: 16555
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2605 -0.6137 -0.1879  0.5196  7.7573 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.4150   0.6442  
##  Residual             0.5803   0.7618  
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##               Estimate Std. Error         df t value Pr(>|t|)  
## (Intercept) -7.702e-15  4.209e-02  2.440e+02   0.000   1.0000  
## scale(phi)  -8.981e-02  4.209e-02  2.440e+02  -2.134   0.0339 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr)
## scale(phi) 0.000
m <-lmer(scale(idtSim) ~ scale(overlap_norm) + ( 1 | subID), data=idSim1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(overlap_norm) + (1 | subID)
##    Data: idSim1
## 
## REML criterion at convergence: 16011.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.6606 -0.6123 -0.1771  0.5399  7.8079 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.02633  0.1623  
##  Residual             0.58030  0.7618  
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##                       Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)         -9.045e-16  1.383e-02  2.440e+02    0.00        1    
## scale(overlap_norm)  6.274e-01  1.383e-02  2.440e+02   45.36   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## scl(vrlp_n) 0.000
m <-lmer(scale(idtSim) ~ scale(H_index) + ( 1 | subID), data=idSim1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(H_index) + (1 | subID)
##    Data: idSim1
## 
## REML criterion at convergence: 16340.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.0934 -0.5980 -0.1875  0.5102  7.8369 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.1600   0.4000  
##  Residual             0.5803   0.7618  
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)    -4.486e-15  2.711e-02  2.440e+02    0.00        1    
## scale(H_index)  5.109e-01  2.711e-02  2.440e+02   18.85   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## scal(H_ndx) 0.000
m <-lmer(scale(idtSim) ~ scale(SE) + ( 1| subID), data=idSim1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(SE) + (1 | subID)
##    Data: idSim1
## 
## REML criterion at convergence: 16496.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2653 -0.6157 -0.1894  0.5188  7.7542 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.4167   0.6455  
##  Residual             0.5809   0.7622  
## Number of obs: 6860, groups:  subID, 245
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)  
## (Intercept) 2.734e-03  4.226e-02 2.430e+02   0.065   0.9485  
## scale(SE)   7.968e-02  4.226e-02 2.430e+02   1.885   0.0606 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##           (Intr)
## scale(SE) 0.000
m <-lmer(scale(idtSim) ~ scale(NFC) + ( 1 | subID), data=idSim1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(NFC) + (1 | subID)
##    Data: idSim1
## 
## REML criterion at convergence: 16551.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2728 -0.6170 -0.1912  0.5182  7.7561 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.4081   0.6388  
##  Residual             0.5803   0.7618  
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##               Estimate Std. Error         df t value Pr(>|t|)   
## (Intercept) -4.217e-15  4.175e-02  2.440e+02   0.000  1.00000   
## scale(NFC)   1.223e-01  4.175e-02  2.440e+02   2.929  0.00372 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr)
## scale(NFC) 0.000
m <-lmer(scale(idtSim) ~ scale(DS) + ( 1 | subID), data=idSim1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(DS) + (1 | subID)
##    Data: idSim1
## 
## REML criterion at convergence: 16557.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2683 -0.6150 -0.1919  0.5192  7.7594 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.4192   0.6475  
##  Residual             0.5803   0.7618  
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##               Estimate Std. Error         df t value Pr(>|t|)
## (Intercept) -5.432e-15  4.229e-02  2.440e+02    0.00     1.00
## scale(DS)   -6.260e-02  4.229e-02  2.440e+02   -1.48     0.14
## 
## Correlation of Fixed Effects:
##           (Intr)
## scale(DS) 0.000
m <-lmer(scale(idtSim) ~ scale(SCC) + ( 1| subID), data=idSim1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(SCC) + (1 | subID)
##    Data: idSim1
## 
## REML criterion at convergence: 16558.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2646 -0.6147 -0.1921  0.5184  7.7602 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.4219   0.6495  
##  Residual             0.5803   0.7618  
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##               Estimate Std. Error         df t value Pr(>|t|)
## (Intercept) -7.412e-15  4.242e-02  2.440e+02   0.000    1.000
## scale(SCC)   3.530e-02  4.242e-02  2.440e+02   0.832    0.406
## 
## Correlation of Fixed Effects:
##            (Intr)
## scale(SCC) 0.000
m <-lmer(scale(idtSim) ~ scale(MemSE) + ( 1 | subID), data=idSim1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(MemSE) + (1 | subID)
##    Data: idSim1
## 
## REML criterion at convergence: 16558.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2624 -0.6133 -0.1924  0.5193  7.7627 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.4205   0.6485  
##  Residual             0.5803   0.7618  
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##                Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)  -6.489e-15  4.235e-02  2.440e+02   0.000     1.00
## scale(MemSE)  5.099e-02  4.236e-02  2.440e+02   1.204     0.23
## 
## Correlation of Fixed Effects:
##             (Intr)
## scale(MmSE) 0.000
m <-lmer(scale(idtSim) ~ scale(PrivCSE) + ( 1 | subID), data=idSim1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(PrivCSE) + (1 | subID)
##    Data: idSim1
## 
## REML criterion at convergence: 16555.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2557 -0.6146 -0.1910  0.5213  7.7609 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.4161   0.6450  
##  Residual             0.5803   0.7618  
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)  
## (Intercept)    -8.270e-15  4.214e-02  2.440e+02    0.00   1.0000  
## scale(PrivCSE)  8.384e-02  4.214e-02  2.440e+02    1.99   0.0478 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## scl(PrvCSE) 0.000
m <-lmer(scale(idtSim) ~ scale(PubCSE) + ( 1 | subID), data=idSim1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(PubCSE) + (1 | subID)
##    Data: idSim1
## 
## REML criterion at convergence: 16554.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2656 -0.6131 -0.1904  0.5211  7.7600 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.4135   0.6431  
##  Residual             0.5803   0.7618  
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##                 Estimate Std. Error         df t value Pr(>|t|)  
## (Intercept)   -7.784e-15  4.201e-02  2.440e+02   0.000   1.0000  
## scale(PubCSE)  9.779e-02  4.202e-02  2.440e+02   2.327   0.0208 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## scal(PbCSE) 0.000
m <-lmer(scale(idtSim) ~ scale(MemSE) + ( 1 | subID), data=idSim1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(MemSE) + (1 | subID)
##    Data: idSim1
## 
## REML criterion at convergence: 16558.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2624 -0.6133 -0.1924  0.5193  7.7627 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.4205   0.6485  
##  Residual             0.5803   0.7618  
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##                Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)  -6.489e-15  4.235e-02  2.440e+02   0.000     1.00
## scale(MemSE)  5.099e-02  4.236e-02  2.440e+02   1.204     0.23
## 
## Correlation of Fixed Effects:
##             (Intr)
## scale(MmSE) 0.000

Individual Differences Predict Pairwise Overlap in Identities in Common

m <-lmer(scale(idSim) ~ scale(SCC) + ( 1 | subID), data=idSim1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(SCC) + (1 | subID)
##    Data: idSim1
## 
## REML criterion at convergence: 15808.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2437 -0.6057 -0.1938  0.3497  4.2370 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.4882   0.6987  
##  Residual             0.5157   0.7181  
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##               Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)  4.883e-15  4.538e-02  2.440e+02   0.000    1.000
## scale(SCC)  -1.252e-03  4.539e-02  2.440e+02  -0.028    0.978
## 
## Correlation of Fixed Effects:
##            (Intr)
## scale(SCC) 0.000
m <-lmer(scale(idSim) ~ scale(Ind) + ( 1 | subID), data=idSim1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(Ind) + (1 | subID)
##    Data: idSim1
## 
## REML criterion at convergence: 15808.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2427 -0.6055 -0.1928  0.3496  4.2380 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.4881   0.6987  
##  Residual             0.5157   0.7181  
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##               Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)  9.267e-15  4.538e-02  2.440e+02   0.000    1.000
## scale(Ind)  -1.030e-02  4.538e-02  2.440e+02  -0.227    0.821
## 
## Correlation of Fixed Effects:
##            (Intr)
## scale(Ind) 0.000
m <-lmer(scale(idSim) ~ scale(Inter) + ( 1 | subID), data=idSim1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(Inter) + (1 | subID)
##    Data: idSim1
## 
## REML criterion at convergence: 15807.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2452 -0.6055 -0.1906  0.3462  4.2403 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.4870   0.6978  
##  Residual             0.5157   0.7181  
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##               Estimate Std. Error        df t value Pr(>|t|)
## (Intercept)  7.909e-15  4.533e-02 2.440e+02   0.000    1.000
## scale(Inter) 3.543e-02  4.533e-02 2.440e+02   0.782    0.435
## 
## Correlation of Fixed Effects:
##             (Intr)
## scale(Intr) 0.000
m <-lmer(scale(idSim) ~ scale(SWLS) + ( 1 | subID), data=idSim1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(SWLS) + (1 | subID)
##    Data: idSim1
## 
## REML criterion at convergence: 15808.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2447 -0.6058 -0.1927  0.3491  4.2381 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.4881   0.6986  
##  Residual             0.5157   0.7181  
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)
## (Intercept) 8.683e-15  4.537e-02 2.440e+02   0.000    1.000
## scale(SWLS) 1.320e-02  4.538e-02 2.440e+02   0.291    0.771
## 
## Correlation of Fixed Effects:
##             (Intr)
## scale(SWLS) 0.000
m <-lmer(scale(idSim) ~ scale(IdImp) + ( 1 | subID), data=idSim1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(IdImp) + (1 | subID)
##    Data: idSim1
## 
## REML criterion at convergence: 15806.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2438 -0.6058 -0.1869  0.3510  4.2439 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.4842   0.6959  
##  Residual             0.5157   0.7181  
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##               Estimate Std. Error        df t value Pr(>|t|)
## (Intercept)  4.354e-15  4.520e-02 2.440e+02   0.000    1.000
## scale(IdImp) 6.303e-02  4.521e-02 2.440e+02   1.394    0.165
## 
## Correlation of Fixed Effects:
##             (Intr)
## scal(IdImp) 0.000
m <-lmer(scale(idSim) ~ scale(phi) + ( 1 | subID), data=idSim1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(phi) + (1 | subID)
##    Data: idSim1
## 
## REML criterion at convergence: 15806.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2458 -0.6070 -0.1889  0.3487  4.2419 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.4846   0.6961  
##  Residual             0.5157   0.7181  
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##               Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)  7.393e-15  4.522e-02  2.440e+02   0.000    1.000
## scale(phi)  -6.035e-02  4.522e-02  2.440e+02  -1.335    0.183
## 
## Correlation of Fixed Effects:
##            (Intr)
## scale(phi) 0.000
m <-lmer(scale(idSim) ~ scale(overlap_norm) + ( 1 | subID), data=idSim1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(overlap_norm) + (1 | subID)
##    Data: idSim1
## 
## REML criterion at convergence: 15791.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2373 -0.6025 -0.1833  0.3453  4.2475 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.4540   0.6738  
##  Residual             0.5157   0.7181  
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##                      Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)         4.184e-15  4.382e-02 2.440e+02   0.000        1    
## scale(overlap_norm) 1.843e-01  4.382e-02 2.440e+02   4.206 3.65e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## scl(vrlp_n) 0.000
m <-lmer(scale(idSim) ~ scale(H_index) + ( 1 | subID), data=idSim1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(H_index) + (1 | subID)
##    Data: idSim1
## 
## REML criterion at convergence: 15781.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2312 -0.6063 -0.1814  0.3389  4.2524 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.4352   0.6597  
##  Residual             0.5157   0.7181  
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##                 Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)    5.340e-15  4.294e-02 2.440e+02   0.000        1    
## scale(H_index) 2.294e-01  4.294e-02 2.440e+02   5.342 2.11e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## scal(H_ndx) 0.000
m <-lmer(scale(idSim) ~ scale(SE) + ( 1| subID), data=idSim1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(SE) + (1 | subID)
##    Data: idSim1
## 
## REML criterion at convergence: 15736
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2444 -0.6061 -0.1920  0.3399  4.2418 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.490    0.7000  
##  Residual             0.515    0.7177  
## Number of obs: 6860, groups:  subID, 245
## 
## Fixed effects:
##               Estimate Std. Error         df t value Pr(>|t|)
## (Intercept) -6.381e-04  4.555e-02  2.430e+02  -0.014    0.989
## scale(SE)   -1.679e-02  4.555e-02  2.430e+02  -0.369    0.713
## 
## Correlation of Fixed Effects:
##           (Intr)
## scale(SE) 0.000
m <-lmer(scale(idSim) ~ scale(NFC) + ( 1 | subID), data=idSim1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(NFC) + (1 | subID)
##    Data: idSim1
## 
## REML criterion at convergence: 15807.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2458 -0.6076 -0.1894  0.3492  4.2414 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.4870   0.6979  
##  Residual             0.5157   0.7181  
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)
## (Intercept) 8.478e-15  4.533e-02 2.440e+02   0.000    1.000
## scale(NFC)  3.482e-02  4.533e-02 2.440e+02   0.768    0.443
## 
## Correlation of Fixed Effects:
##            (Intr)
## scale(NFC) 0.000
m <-lmer(scale(idSim) ~ scale(DS) + ( 1 | subID), data=idSim1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(DS) + (1 | subID)
##    Data: idSim1
## 
## REML criterion at convergence: 15807.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2496 -0.6042 -0.1896  0.3510  4.2412 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.4864   0.6974  
##  Residual             0.5157   0.7181  
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##               Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)  1.022e-14  4.530e-02  2.440e+02   0.000    1.000
## scale(DS)   -4.289e-02  4.530e-02  2.440e+02  -0.947    0.345
## 
## Correlation of Fixed Effects:
##           (Intr)
## scale(DS) 0.000
m <-lmer(scale(idSim) ~ scale(SCC) + ( 1| subID), data=idSim1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(SCC) + (1 | subID)
##    Data: idSim1
## 
## REML criterion at convergence: 15808.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2437 -0.6057 -0.1938  0.3497  4.2370 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.4882   0.6987  
##  Residual             0.5157   0.7181  
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##               Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)  4.883e-15  4.538e-02  2.440e+02   0.000    1.000
## scale(SCC)  -1.252e-03  4.539e-02  2.440e+02  -0.028    0.978
## 
## Correlation of Fixed Effects:
##            (Intr)
## scale(SCC) 0.000
m <-lmer(scale(idSim) ~ scale(MemSE) + ( 1 | subID), data=idSim1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(MemSE) + (1 | subID)
##    Data: idSim1
## 
## REML criterion at convergence: 15808.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2440 -0.6057 -0.1932  0.3499  4.2376 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.4882   0.6987  
##  Residual             0.5157   0.7181  
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##               Estimate Std. Error        df t value Pr(>|t|)
## (Intercept)  9.275e-15  4.538e-02 2.440e+02   0.000    1.000
## scale(MemSE) 6.208e-03  4.538e-02 2.440e+02   0.137    0.891
## 
## Correlation of Fixed Effects:
##             (Intr)
## scale(MmSE) 0.000
m <-lmer(scale(idSim) ~ scale(PrivCSE) + ( 1 | subID), data=idSim1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(PrivCSE) + (1 | subID)
##    Data: idSim1
## 
## REML criterion at convergence: 15807.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2471 -0.6060 -0.1898  0.3511  4.2410 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.4861   0.6972  
##  Residual             0.5157   0.7181  
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##                 Estimate Std. Error        df t value Pr(>|t|)
## (Intercept)    1.105e-14  4.529e-02 2.440e+02   0.000    1.000
## scale(PrivCSE) 4.581e-02  4.529e-02 2.440e+02   1.011    0.313
## 
## Correlation of Fixed Effects:
##             (Intr)
## scl(PrvCSE) 0.000
m <-lmer(scale(idSim) ~ scale(PubCSE) + ( 1 | subID), data=idSim1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(PubCSE) + (1 | subID)
##    Data: idSim1
## 
## REML criterion at convergence: 15808.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2440 -0.6057 -0.1939  0.3497  4.2369 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.4882   0.6987  
##  Residual             0.5157   0.7181  
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##                Estimate Std. Error        df t value Pr(>|t|)
## (Intercept)   5.889e-15  4.538e-02 2.440e+02   0.000    1.000
## scale(PubCSE) 2.111e-03  4.539e-02 2.440e+02   0.047    0.963
## 
## Correlation of Fixed Effects:
##             (Intr)
## scal(PbCSE) 0.000
m <-lmer(scale(idSim) ~ scale(MemSE) + ( 1 | subID), data=idSim1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(MemSE) + (1 | subID)
##    Data: idSim1
## 
## REML criterion at convergence: 15808.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2440 -0.6057 -0.1932  0.3499  4.2376 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.4882   0.6987  
##  Residual             0.5157   0.7181  
## Number of obs: 6888, groups:  subID, 246
## 
## Fixed effects:
##               Estimate Std. Error        df t value Pr(>|t|)
## (Intercept)  9.275e-15  4.538e-02 2.440e+02   0.000    1.000
## scale(MemSE) 6.208e-03  4.538e-02 2.440e+02   0.137    0.891
## 
## Correlation of Fixed Effects:
##             (Intr)
## scale(MmSE) 0.000

Study 2

Positive

m <-lmer(scale(idtpSim) ~ scale(SCC) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(SCC) + scale(idtnSim) + (scale(idtnSim) |  
##     subID)
##    Data: idSim2
## 
## REML criterion at convergence: 16875.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.4297 -0.6105 -0.2583  0.4996  6.8372 
## 
## Random effects:
##  Groups   Name           Variance Std.Dev. Corr
##  subID    (Intercept)    0.37363  0.6113       
##           scale(idtnSim) 0.01323  0.1150   0.13
##  Residual                0.59745  0.7729       
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)     -0.004870   0.040195 239.991833  -0.121 0.903673    
## scale(SCC)      -0.007779   0.040025 231.013660  -0.194 0.846062    
## scale(idtnSim)   0.048476   0.014208 245.034120   3.412 0.000754 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) s(SCC)
## scale(SCC)  0.000        
## scal(dtnSm) 0.068  0.010
m <-lmer(scale(idtpSim) ~ scale(Ind) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(Ind) + scale(idtnSim) + (scale(idtnSim) |  
##     subID)
##    Data: idSim2
## 
## REML criterion at convergence: 16875.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.4303 -0.6112 -0.2576  0.4998  6.8381 
## 
## Random effects:
##  Groups   Name           Variance Std.Dev. Corr
##  subID    (Intercept)    0.37362  0.6112       
##           scale(idtnSim) 0.01323  0.1150   0.13
##  Residual                0.59744  0.7729       
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)     -0.004853   0.040195 239.953560  -0.121 0.904005    
## scale(Ind)       0.006731   0.040008 230.700716   0.168 0.866543    
## scale(idtnSim)   0.048523   0.014208 245.017466   3.415 0.000746 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(I)
## scale(Ind)  -0.001       
## scal(dtnSm)  0.067  0.009
m <-lmer(scale(idtpSim) ~ scale(Inter) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(Inter) + scale(idtnSim) + (scale(idtnSim) |  
##     subID)
##    Data: idSim2
## 
## REML criterion at convergence: 16875.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.4297 -0.6116 -0.2578  0.5004  6.8337 
## 
## Random effects:
##  Groups   Name           Variance Std.Dev. Corr
##  subID    (Intercept)    0.37315  0.6109       
##           scale(idtnSim) 0.01319  0.1148   0.13
##  Residual                0.59746  0.7730       
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)     -0.004877   0.040171 240.078598  -0.121  0.90347    
## scale(Inter)     0.024944   0.040110 236.395194   0.622  0.53461    
## scale(idtnSim)   0.048525   0.014200 245.126239   3.417  0.00074 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(I)
## scale(Intr) -0.001       
## scal(dtnSm)  0.067  0.001
m <-lmer(scale(idtpSim) ~ scale(SWLS) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(SWLS) + scale(idtnSim) + (scale(idtnSim) |  
##     subID)
##    Data: idSim2
## 
## REML criterion at convergence: 16875.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.4301 -0.6107 -0.2591  0.4990  6.8356 
## 
## Random effects:
##  Groups   Name           Variance Std.Dev. Corr
##  subID    (Intercept)    0.37334  0.6110       
##           scale(idtnSim) 0.01321  0.1150   0.13
##  Residual                0.59745  0.7729       
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)     -0.004816   0.040181 239.936277  -0.120 0.904692    
## scale(SWLS)     -0.018150   0.040073 234.198236  -0.453 0.651018    
## scale(idtnSim)   0.048572   0.014205 244.958608   3.419 0.000735 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) s(SWLS
## scale(SWLS)  0.001       
## scal(dtnSm)  0.065 -0.010
m <-lmer(scale(idtpSim) ~ scale(IdImp) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(IdImp) + scale(idtnSim) + (scale(idtnSim) |  
##     subID)
##    Data: idSim2
## 
## REML criterion at convergence: 16872
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.4335 -0.6122 -0.2593  0.5003  6.8393 
## 
## Random effects:
##  Groups   Name           Variance Std.Dev. Corr
##  subID    (Intercept)    0.36767  0.6064       
##           scale(idtnSim) 0.01333  0.1154   0.12
##  Residual                0.59739  0.7729       
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)     -0.004651   0.039896 239.835555  -0.117 0.907301    
## scale(IdImp)     0.076797   0.039746 232.098472   1.932 0.054549 .  
## scale(idtnSim)   0.048393   0.014220 244.624809   3.403 0.000778 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) sc(II)
## scal(IdImp)  0.001       
## scal(dtnSm)  0.061 -0.010
m <-lmer(scale(idtpSim) ~ scale(phi) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(phi) + scale(idtnSim) + (scale(idtnSim) |  
##     subID)
##    Data: idSim2
## 
## REML criterion at convergence: 16867.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.4425 -0.6128 -0.2607  0.4968  6.8570 
## 
## Random effects:
##  Groups   Name           Variance Std.Dev. Corr
##  subID    (Intercept)    0.36127  0.6011       
##           scale(idtnSim) 0.01304  0.1142   0.10
##  Residual                0.59747  0.7730       
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)     -0.004135   0.039568 239.928621  -0.105 0.916851    
## scale(phi)      -0.112466   0.039461 233.395432  -2.850 0.004762 ** 
## scale(idtnSim)   0.047844   0.014174 245.777641   3.375 0.000856 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(p)
## scale(phi)  -0.002       
## scal(dtnSm)  0.051  0.025
m <-lmer(scale(idtpSim) ~ scale(overlap_norm) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## scale(idtpSim) ~ scale(overlap_norm) + scale(idtnSim) + (scale(idtnSim) |  
##     subID)
##    Data: idSim2
## 
## REML criterion at convergence: 16431.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.3279 -0.6247 -0.2276  0.5101  6.7841 
## 
## Random effects:
##  Groups   Name           Variance Std.Dev. Corr 
##  subID    (Intercept)    0.04302  0.2074        
##           scale(idtnSim) 0.01085  0.1042   -0.36
##  Residual                0.59799  0.7733        
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##                      Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)           0.00154    0.01656 243.05209   0.093 0.925988    
## scale(overlap_norm)   0.58420    0.01618 225.57025  36.109  < 2e-16 ***
## scale(idtnSim)        0.04391    0.01288 279.25252   3.409 0.000748 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(_)
## scl(vrlp_n) -0.003       
## scal(dtnSm) -0.156 -0.119
m <-lmer(scale(idtpSim) ~ scale(H_index) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(H_index) + scale(idtnSim) + (scale(idtnSim) |  
##     subID)
##    Data: idSim2
## 
## REML criterion at convergence: 16691.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.2376 -0.6187 -0.2504  0.4861  6.8250 
## 
## Random effects:
##  Groups   Name           Variance Std.Dev. Corr 
##  subID    (Intercept)    0.16524  0.4065        
##           scale(idtnSim) 0.01323  0.1150   -0.09
##  Residual                0.59706  0.7727        
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)     -0.001734   0.027790 239.474808  -0.062  0.95031    
## scale(H_index)   0.463588   0.027751 245.282449  16.705  < 2e-16 ***
## scale(idtnSim)   0.044565   0.013990 255.211824   3.186  0.00162 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) sc(H_)
## scal(H_ndx)  0.000       
## scal(dtnSm) -0.045 -0.070
m <-lmer(scale(idtpSim) ~ scale(SE) + scale(idtnSim) + ( scale(idtnSim)| subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(SE) + scale(idtnSim) + (scale(idtnSim) |  
##     subID)
##    Data: idSim2
## 
## REML criterion at convergence: 16875.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.4299 -0.6122 -0.2592  0.4999  6.8406 
## 
## Random effects:
##  Groups   Name           Variance Std.Dev. Corr
##  subID    (Intercept)    0.37304  0.6108       
##           scale(idtnSim) 0.01323  0.1150   0.13
##  Residual                0.59745  0.7729       
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)     -0.004892   0.040165 239.937004  -0.122 0.903160    
## scale(SE)        0.024422   0.040004 230.805493   0.611 0.542129    
## scale(idtnSim)   0.048521   0.014207 245.069511   3.415 0.000746 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) sc(SE)
## scale(SE)   0.001        
## scal(dtnSm) 0.069  0.000
m <-lmer(scale(idtpSim) ~ scale(NFC) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(NFC) + scale(idtnSim) + (scale(idtnSim) |  
##     subID)
##    Data: idSim2
## 
## REML criterion at convergence: 16875.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.4297 -0.6105 -0.2577  0.4995  6.8385 
## 
## Random effects:
##  Groups   Name           Variance Std.Dev. Corr
##  subID    (Intercept)    0.37348  0.6111       
##           scale(idtnSim) 0.01324  0.1151   0.13
##  Residual                0.59744  0.7729       
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)     -0.004868   0.040188 239.944596  -0.121 0.903684    
## scale(NFC)       0.012812   0.040011 230.989555   0.320 0.749091    
## scale(idtnSim)   0.048547   0.014209 244.958441   3.417 0.000742 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) s(NFC)
## scale(NFC)  -0.001       
## scal(dtnSm)  0.067  0.009
m <-lmer(scale(idtpSim) ~ scale(DS) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(DS) + scale(idtnSim) + (scale(idtnSim) |  
##     subID)
##    Data: idSim2
## 
## REML criterion at convergence: 16874
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.4301 -0.6114 -0.2613  0.5004  6.8282 
## 
## Random effects:
##  Groups   Name           Variance Std.Dev. Corr
##  subID    (Intercept)    0.37115  0.6092       
##           scale(idtnSim) 0.01313  0.1146   0.12
##  Residual                0.59747  0.7730       
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)     -0.004753   0.040070 240.094424  -0.119 0.905673    
## scale(DS)        0.051613   0.039964 233.544409   1.291 0.197810    
## scale(idtnSim)   0.048425   0.014191 245.402535   3.412 0.000753 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) sc(DS)
## scale(DS)    0.000       
## scal(dtnSm)  0.063 -0.008
m <-lmer(scale(idtpSim) ~ scale(SCC) + scale(idtnSim) + ( scale(idtnSim)| subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(SCC) + scale(idtnSim) + (scale(idtnSim) |  
##     subID)
##    Data: idSim2
## 
## REML criterion at convergence: 16875.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.4297 -0.6105 -0.2583  0.4996  6.8372 
## 
## Random effects:
##  Groups   Name           Variance Std.Dev. Corr
##  subID    (Intercept)    0.37363  0.6113       
##           scale(idtnSim) 0.01323  0.1150   0.13
##  Residual                0.59745  0.7729       
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)     -0.004870   0.040195 239.991833  -0.121 0.903673    
## scale(SCC)      -0.007779   0.040025 231.013660  -0.194 0.846062    
## scale(idtnSim)   0.048476   0.014208 245.034120   3.412 0.000754 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) s(SCC)
## scale(SCC)  0.000        
## scal(dtnSm) 0.068  0.010
m <-lmer(scale(idtpSim) ~ scale(MemSE) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(MemSE) + scale(idtnSim) + (scale(idtnSim) |  
##     subID)
##    Data: idSim2
## 
## REML criterion at convergence: 16874.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.4250 -0.6120 -0.2553  0.5019  6.8431 
## 
## Random effects:
##  Groups   Name           Variance Std.Dev. Corr
##  subID    (Intercept)    0.37149  0.6095       
##           scale(idtnSim) 0.01326  0.1152   0.13
##  Residual                0.59743  0.7729       
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)     -0.004925   0.040088 239.919466  -0.123 0.902315    
## scale(MemSE)     0.045983   0.039932 232.691863   1.152 0.250699    
## scale(idtnSim)   0.048829   0.014213 244.906886   3.436 0.000694 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) s(MSE)
## scale(MmSE) -0.001       
## scal(dtnSm)  0.068  0.016
m <-lmer(scale(idtpSim) ~ scale(PrivCSE) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(PrivCSE) + scale(idtnSim) + (scale(idtnSim) |  
##     subID)
##    Data: idSim2
## 
## REML criterion at convergence: 16873.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.4292 -0.6132 -0.2561  0.4995  6.8420 
## 
## Random effects:
##  Groups   Name           Variance Std.Dev. Corr
##  subID    (Intercept)    0.37046  0.6087       
##           scale(idtnSim) 0.01323  0.1150   0.12
##  Residual                0.59744  0.7729       
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)     -0.004921   0.040036 239.950353  -0.123 0.902282    
## scale(PrivCSE)   0.056022   0.039887 232.377855   1.405 0.161505    
## scale(idtnSim)   0.049049   0.014209 245.251014   3.452 0.000655 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) s(PCSE
## scl(PrvCSE) -0.002       
## scal(dtnSm)  0.064  0.023
m <-lmer(scale(idtpSim) ~ scale(PubCSE) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(PubCSE) + scale(idtnSim) + (scale(idtnSim) |  
##     subID)
##    Data: idSim2
## 
## REML criterion at convergence: 16875.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.4303 -0.6131 -0.2571  0.4994  6.8387 
## 
## Random effects:
##  Groups   Name           Variance Std.Dev. Corr
##  subID    (Intercept)    0.37337  0.6110       
##           scale(idtnSim) 0.01321  0.1149   0.13
##  Residual                0.59745  0.7729       
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)     -0.004809   0.040183 239.944168  -0.120 0.904842    
## scale(PubCSE)    0.017806   0.039999 231.162936   0.445 0.656623    
## scale(idtnSim)   0.048547   0.014205 245.113542   3.418 0.000739 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) s(PCSE
## scal(PbCSE) 0.000        
## scal(dtnSm) 0.066  0.007
m <-lmer(scale(idtpSim) ~ scale(MemSE) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(MemSE) + scale(idtnSim) + (scale(idtnSim) |  
##     subID)
##    Data: idSim2
## 
## REML criterion at convergence: 16874.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.4250 -0.6120 -0.2553  0.5019  6.8431 
## 
## Random effects:
##  Groups   Name           Variance Std.Dev. Corr
##  subID    (Intercept)    0.37149  0.6095       
##           scale(idtnSim) 0.01326  0.1152   0.13
##  Residual                0.59743  0.7729       
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)     -0.004925   0.040088 239.919466  -0.123 0.902315    
## scale(MemSE)     0.045983   0.039932 232.691863   1.152 0.250699    
## scale(idtnSim)   0.048829   0.014213 244.906886   3.436 0.000694 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) s(MSE)
## scale(MmSE) -0.001       
## scal(dtnSm)  0.068  0.016

Negative

m <-lmer(scale(idtnSim) ~ scale(SCC) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(SCC) + scale(idtpSim) + (scale(idtpSim) |  
##     subID)
##    Data: idSim2
## 
## REML criterion at convergence: 17114.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3228 -0.6306 -0.2199  0.4583  3.8839 
## 
## Random effects:
##  Groups   Name           Variance Std.Dev. Corr
##  subID    (Intercept)    0.35107  0.5925       
##           scale(idtpSim) 0.01435  0.1198   0.08
##  Residual                0.62067  0.7878       
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)     -0.003114   0.039087 243.531088  -0.080   0.9366    
## scale(SCC)      -0.045713   0.039033 242.420497  -1.171   0.2427    
## scale(idtpSim)   0.057455   0.015168 204.081703   3.788   0.0002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) s(SCC)
## scale(SCC)  0.000        
## scal(dtpSm) 0.052  0.004
m <-lmer(scale(idtnSim) ~ scale(Ind) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(Ind) + scale(idtpSim) + (scale(idtpSim) |  
##     subID)
##    Data: idSim2
## 
## REML criterion at convergence: 17114.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3224 -0.6300 -0.2198  0.4593  3.8837 
## 
## Random effects:
##  Groups   Name           Variance Std.Dev. Corr
##  subID    (Intercept)    0.35203  0.5933       
##           scale(idtpSim) 0.01434  0.1198   0.08
##  Residual                0.62067  0.7878       
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)     -0.003069   0.039137 243.563991  -0.078 0.937567    
## scale(Ind)      -0.034451   0.039025 240.003313  -0.883 0.378234    
## scale(idtpSim)   0.057506   0.015166 203.990240   3.792 0.000197 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(I)
## scale(Ind)  -0.001       
## scal(dtpSm)  0.052 -0.001
m <-lmer(scale(idtnSim) ~ scale(Inter) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00245935 (tol = 0.002, component 1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(Inter) + scale(idtpSim) + (scale(idtpSim) |  
##     subID)
##    Data: idSim2
## 
## REML criterion at convergence: 17115.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3224 -0.6303 -0.2207  0.4591  3.8850 
## 
## Random effects:
##  Groups   Name           Variance Std.Dev. Corr
##  subID    (Intercept)    0.35299  0.5941       
##           scale(idtpSim) 0.01441  0.1201   0.07
##  Residual                0.62064  0.7878       
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)     -0.003067   0.039188 243.542691  -0.078 0.937690    
## scale(Inter)    -0.009883   0.039155 242.815304  -0.252 0.800944    
## scale(idtpSim)   0.057542   0.015178 203.389925   3.791 0.000198 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(I)
## scale(Intr)  0.002       
## scal(dtpSm)  0.050 -0.007
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00245935 (tol = 0.002, component 1)
m <-lmer(scale(idtnSim) ~ scale(SWLS) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(SWLS) + scale(idtpSim) + (scale(idtpSim) |  
##     subID)
##    Data: idSim2
## 
## REML criterion at convergence: 17114.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3230 -0.6316 -0.2207  0.4597  3.8885 
## 
## Random effects:
##  Groups   Name           Variance Std.Dev. Corr
##  subID    (Intercept)    0.35192  0.5932       
##           scale(idtpSim) 0.01439  0.1200   0.08
##  Residual                0.62065  0.7878       
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)     -0.003189   0.039131 243.509224  -0.081 0.935113    
## scale(SWLS)      0.035244   0.039009 240.387348   0.903 0.367173    
## scale(idtpSim)   0.057579   0.015176 203.867312   3.794 0.000195 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) s(SWLS
## scale(SWLS) -0.001       
## scal(dtpSm)  0.053  0.007
m <-lmer(scale(idtnSim) ~ scale(IdImp) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(IdImp) + scale(idtpSim) + (scale(idtpSim) |  
##     subID)
##    Data: idSim2
## 
## REML criterion at convergence: 17115
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3216 -0.6304 -0.2206  0.4594  3.8855 
## 
## Random effects:
##  Groups   Name           Variance Std.Dev. Corr
##  subID    (Intercept)    0.35250  0.5937       
##           scale(idtpSim) 0.01436  0.1198   0.07
##  Residual                0.62066  0.7878       
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##                 Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)     -0.00296    0.03916 243.61565  -0.076 0.939817    
## scale(IdImp)     0.02604    0.03911 242.27078   0.666 0.506150    
## scale(idtpSim)   0.05732    0.01517 204.00290   3.778 0.000207 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) sc(II)
## scal(IdImp)  0.002       
## scal(dtpSm)  0.049 -0.022
m <-lmer(scale(idtnSim) ~ scale(phi) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(phi) + scale(idtpSim) + (scale(idtpSim) |  
##     subID)
##    Data: idSim2
## 
## REML criterion at convergence: 17112.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3187 -0.6322 -0.2210  0.4576  3.8874 
## 
## Random effects:
##  Groups   Name           Variance Std.Dev. Corr
##  subID    (Intercept)    0.34863  0.5904       
##           scale(idtpSim) 0.01421  0.1192   0.05
##  Residual                0.62069  0.7878       
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)     -0.002525   0.038960 243.909771  -0.065 0.948373    
## scale(phi)      -0.068932   0.038923 241.059574  -1.771 0.077828 .  
## scale(idtpSim)   0.056774   0.015148 204.734937   3.748 0.000232 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(p)
## scale(phi)  -0.004       
## scal(dtpSm)  0.041  0.037
m <-lmer(scale(idtnSim) ~ scale(overlap_norm) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## scale(idtnSim) ~ scale(overlap_norm) + scale(idtpSim) + (scale(idtpSim) |  
##     subID)
##    Data: idSim2
## 
## REML criterion at convergence: 17098.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3055 -0.6353 -0.2204  0.4564  3.8905 
## 
## Random effects:
##  Groups   Name           Variance Std.Dev. Corr
##  subID    (Intercept)    0.32996  0.5744       
##           scale(idtpSim) 0.01375  0.1173   0.03
##  Residual                0.62072  0.7879       
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##                       Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)         -2.344e-05  3.798e-02  2.467e+02  -0.001  0.99951    
## scale(overlap_norm)  1.665e-01  3.950e-02  2.685e+02   4.215 3.42e-05 ***
## scale(idtpSim)       4.812e-02  1.525e-02  2.145e+02   3.156  0.00183 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(_)
## scl(vrlp_n)  0.015       
## scal(dtpSm)  0.027 -0.162
m <-lmer(scale(idtnSim) ~ scale(H_index) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(H_index) + scale(idtpSim) + (scale(idtpSim) |  
##     subID)
##    Data: idSim2
## 
## REML criterion at convergence: 17101
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3080 -0.6407 -0.2158  0.4612  3.8922 
## 
## Random effects:
##  Groups   Name           Variance Std.Dev. Corr
##  subID    (Intercept)    0.33343  0.5774       
##           scale(idtpSim) 0.01334  0.1155   0.04
##  Residual                0.62094  0.7880       
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)     -0.001067   0.038155 245.685802  -0.028 0.977704    
## scale(H_index)   0.150428   0.039126 258.420077   3.845 0.000152 ***
## scale(idtpSim)   0.050106   0.015132 207.720234   3.311 0.001095 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) sc(H_)
## scal(H_ndx)  0.010       
## scal(dtpSm)  0.034 -0.140
m <-lmer(scale(idtnSim) ~ scale(SE) + scale(idtpSim) + ( scale(idtpSim)| subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(SE) + scale(idtpSim) + (scale(idtpSim) |  
##     subID)
##    Data: idSim2
## 
## REML criterion at convergence: 17115.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3222 -0.6301 -0.2206  0.4597  3.8843 
## 
## Random effects:
##  Groups   Name           Variance Std.Dev. Corr
##  subID    (Intercept)    0.35312  0.5942       
##           scale(idtpSim) 0.01437  0.1199   0.07
##  Residual                0.62066  0.7878       
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)     -0.003036   0.039194 243.577999  -0.077 0.938315    
## scale(SE)       -0.003541   0.039110 241.250555  -0.091 0.927924    
## scale(idtpSim)   0.057508   0.015170 203.857175   3.791 0.000198 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) sc(SE)
## scale(SE)    0.000       
## scal(dtpSm)  0.050 -0.003
m <-lmer(scale(idtnSim) ~ scale(NFC) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(NFC) + scale(idtpSim) + (scale(idtpSim) |  
##     subID)
##    Data: idSim2
## 
## REML criterion at convergence: 17114.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3222 -0.6305 -0.2224  0.4599  3.8849 
## 
## Random effects:
##  Groups   Name           Variance Std.Dev. Corr
##  subID    (Intercept)    0.35220  0.5935       
##           scale(idtpSim) 0.01431  0.1196   0.07
##  Residual                0.62068  0.7878       
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)     -0.003018   0.039146 243.482429  -0.077 0.938609    
## scale(NFC)      -0.030124   0.039081 242.089316  -0.771 0.441568    
## scale(idtpSim)   0.057569   0.015161 204.014551   3.797 0.000193 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) s(NFC)
## scale(NFC)  -0.001       
## scal(dtpSm)  0.050 -0.004
m <-lmer(scale(idtnSim) ~ scale(DS) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(DS) + scale(idtpSim) + (scale(idtpSim) |  
##     subID)
##    Data: idSim2
## 
## REML criterion at convergence: 17115
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3211 -0.6308 -0.2210  0.4577  3.8843 
## 
## Random effects:
##  Groups   Name           Variance Std.Dev. Corr
##  subID    (Intercept)    0.35260  0.5938       
##           scale(idtpSim) 0.01426  0.1194   0.07
##  Residual                0.62070  0.7878       
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)     -0.002947   0.039166 243.605804  -0.075 0.940085    
## scale(DS)        0.024557   0.039290 246.031542   0.625 0.532534    
## scale(idtpSim)   0.057373   0.015154 203.871968   3.786 0.000201 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) sc(DS)
## scale(DS)    0.003       
## scal(dtpSm)  0.049 -0.015
m <-lmer(scale(idtnSim) ~ scale(SCC) + scale(idtpSim) + ( scale(idtpSim)| subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(SCC) + scale(idtpSim) + (scale(idtpSim) |  
##     subID)
##    Data: idSim2
## 
## REML criterion at convergence: 17114.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3228 -0.6306 -0.2199  0.4583  3.8839 
## 
## Random effects:
##  Groups   Name           Variance Std.Dev. Corr
##  subID    (Intercept)    0.35107  0.5925       
##           scale(idtpSim) 0.01435  0.1198   0.08
##  Residual                0.62067  0.7878       
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)     -0.003114   0.039087 243.531088  -0.080   0.9366    
## scale(SCC)      -0.045713   0.039033 242.420497  -1.171   0.2427    
## scale(idtpSim)   0.057455   0.015168 204.081703   3.788   0.0002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) s(SCC)
## scale(SCC)  0.000        
## scal(dtpSm) 0.052  0.004
m <-lmer(scale(idtnSim) ~ scale(MemSE) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(MemSE) + scale(idtpSim) + (scale(idtpSim) |  
##     subID)
##    Data: idSim2
## 
## REML criterion at convergence: 17112.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3237 -0.6290 -0.2199  0.4586  3.8899 
## 
## Random effects:
##  Groups   Name           Variance Std.Dev. Corr
##  subID    (Intercept)    0.34902  0.5908       
##           scale(idtpSim) 0.01443  0.1201   0.07
##  Residual                0.62065  0.7878       
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)     -0.003076   0.038982 243.316102  -0.079 0.937172    
## scale(MemSE)    -0.061996   0.038966 242.802001  -1.591 0.112902    
## scale(idtpSim)   0.057976   0.015179 204.272822   3.820 0.000177 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) s(MSE)
## scale(MmSE)  0.001       
## scal(dtpSm)  0.049 -0.013
m <-lmer(scale(idtnSim) ~ scale(PrivCSE) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(PrivCSE) + scale(idtpSim) + (scale(idtpSim) |  
##     subID)
##    Data: idSim2
## 
## REML criterion at convergence: 17110.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3256 -0.6310 -0.2192  0.4593  3.8941 
## 
## Random effects:
##  Groups   Name           Variance Std.Dev. Corr
##  subID    (Intercept)    0.34478  0.5872       
##           scale(idtpSim) 0.01442  0.1201   0.08
##  Residual                0.62067  0.7878       
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)     -0.003396   0.038760 243.061976  -0.088 0.930245    
## scale(PrivCSE)  -0.088932   0.038752 242.932615  -2.295 0.022592 *  
## scale(idtpSim)   0.058256   0.015178 204.368511   3.838 0.000165 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) s(PCSE
## scl(PrvCSE)  0.002       
## scal(dtpSm)  0.055 -0.016
m <-lmer(scale(idtnSim) ~ scale(PubCSE) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(PubCSE) + scale(idtpSim) + (scale(idtpSim) |  
##     subID)
##    Data: idSim2
## 
## REML criterion at convergence: 17115.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3223 -0.6293 -0.2202  0.4598  3.8842 
## 
## Random effects:
##  Groups   Name           Variance Std.Dev. Corr
##  subID    (Intercept)    0.35286  0.5940       
##           scale(idtpSim) 0.01435  0.1198   0.08
##  Residual                0.62067  0.7878       
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)     -0.003085   0.039180 243.541210  -0.079 0.937296    
## scale(PubCSE)   -0.017932   0.039096 241.286396  -0.459 0.646878    
## scale(idtpSim)   0.057515   0.015169 203.930805   3.792 0.000197 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) s(PCSE
## scal(PbCSE) -0.001       
## scal(dtpSm)  0.052 -0.006
m <-lmer(scale(idtnSim) ~ scale(MemSE) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(MemSE) + scale(idtpSim) + (scale(idtpSim) |  
##     subID)
##    Data: idSim2
## 
## REML criterion at convergence: 17112.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3237 -0.6290 -0.2199  0.4586  3.8899 
## 
## Random effects:
##  Groups   Name           Variance Std.Dev. Corr
##  subID    (Intercept)    0.34902  0.5908       
##           scale(idtpSim) 0.01443  0.1201   0.07
##  Residual                0.62065  0.7878       
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)     -0.003076   0.038982 243.316102  -0.079 0.937172    
## scale(MemSE)    -0.061996   0.038966 242.802001  -1.591 0.112902    
## scale(idtpSim)   0.057976   0.015179 204.272822   3.820 0.000177 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) s(MSE)
## scale(MmSE)  0.001       
## scal(dtpSm)  0.049 -0.013

Both

m <-lmer(scale(idtSim) ~ scale(SCC) + ( 1 | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(SCC) + (1 | subID)
##    Data: idSim2
## 
## REML criterion at convergence: 16838.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.6165 -0.6416 -0.2314  0.5058  7.9550 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.4033   0.6350  
##  Residual             0.6000   0.7746  
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##               Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)  1.547e-14  4.147e-02  2.450e+02   0.000    1.000
## scale(SCC)  -9.042e-03  4.147e-02  2.450e+02  -0.218    0.828
## 
## Correlation of Fixed Effects:
##            (Intr)
## scale(SCC) 0.000
m <-lmer(scale(idtSim) ~ scale(Ind) + ( 1 | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(Ind) + (1 | subID)
##    Data: idSim2
## 
## REML criterion at convergence: 16838.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.6164 -0.6406 -0.2313  0.5061  7.9555 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.4032   0.6350  
##  Residual             0.6000   0.7746  
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)
## (Intercept) 1.389e-14  4.146e-02 2.450e+02   0.000    1.000
## scale(Ind)  1.146e-02  4.147e-02 2.450e+02   0.276    0.783
## 
## Correlation of Fixed Effects:
##            (Intr)
## scale(Ind) 0.000
m <-lmer(scale(idtSim) ~ scale(Inter) + ( 1 | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(Inter) + (1 | subID)
##    Data: idSim2
## 
## REML criterion at convergence: 16837.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.6166 -0.6403 -0.2326  0.5069  7.9548 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.4015   0.6336  
##  Residual             0.6000   0.7746  
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##               Estimate Std. Error        df t value Pr(>|t|)
## (Intercept)  1.461e-14  4.138e-02 2.450e+02   0.000    1.000
## scale(Inter) 4.306e-02  4.138e-02 2.450e+02   1.041    0.299
## 
## Correlation of Fixed Effects:
##             (Intr)
## scale(Intr) 0.000
m <-lmer(scale(idtSim) ~ scale(SWLS) + ( 1 | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(SWLS) + (1 | subID)
##    Data: idSim2
## 
## REML criterion at convergence: 16837.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.6150 -0.6402 -0.2320  0.5064  7.9506 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.4027   0.6346  
##  Residual             0.6000   0.7746  
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##               Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)  1.355e-14  4.144e-02  2.450e+02   0.000    1.000
## scale(SWLS) -2.505e-02  4.144e-02  2.450e+02  -0.604    0.546
## 
## Correlation of Fixed Effects:
##             (Intr)
## scale(SWLS) 0.000
m <-lmer(scale(idtSim) ~ scale(IdImp) + ( 1 | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(IdImp) + (1 | subID)
##    Data: idSim2
## 
## REML criterion at convergence: 16835.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.6158 -0.6404 -0.2316  0.5046  7.9560 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.3987   0.6314  
##  Residual             0.6000   0.7746  
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##               Estimate Std. Error        df t value Pr(>|t|)
## (Intercept)  1.467e-14  4.124e-02 2.450e+02   0.000    1.000
## scale(IdImp) 6.760e-02  4.125e-02 2.450e+02   1.639    0.103
## 
## Correlation of Fixed Effects:
##             (Intr)
## scal(IdImp) 0.000
m <-lmer(scale(idtSim) ~ scale(phi) + ( 1 | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(phi) + (1 | subID)
##    Data: idSim2
## 
## REML criterion at convergence: 16833.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.6193 -0.6376 -0.2324  0.5064  7.9523 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.3953   0.6288  
##  Residual             0.6000   0.7746  
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##               Estimate Std. Error         df t value Pr(>|t|)  
## (Intercept)  1.426e-14  4.108e-02  2.450e+02   0.000   1.0000  
## scale(phi)  -8.908e-02  4.108e-02  2.450e+02  -2.168   0.0311 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr)
## scale(phi) 0.000
m <-lmer(scale(idtSim) ~ scale(overlap_norm) + ( 1 | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(overlap_norm) + (1 | subID)
##    Data: idSim2
## 
## REML criterion at convergence: 16214.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.5350 -0.6414 -0.1935  0.5300  8.0647 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.01193  0.1092  
##  Residual             0.59996  0.7746  
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##                      Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)         1.917e-15  1.162e-02 2.450e+02    0.00        1    
## scale(overlap_norm) 6.231e-01  1.162e-02 2.450e+02   53.61   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## scl(vrlp_n) 0.000
m <-lmer(scale(idtSim) ~ scale(H_index) + ( 1 | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(H_index) + (1 | subID)
##    Data: idSim2
## 
## REML criterion at convergence: 16638.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.3419 -0.6418 -0.2390  0.4970  8.0199 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.1665   0.4080  
##  Residual             0.6000   0.7746  
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##                 Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)    6.823e-15  2.758e-02 2.450e+02    0.00        1    
## scale(H_index) 4.848e-01  2.758e-02 2.450e+02   17.57   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## scal(H_ndx) 0.000
m <-lmer(scale(idtSim) ~ scale(SE) + ( 1| subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(SE) + (1 | subID)
##    Data: idSim2
## 
## REML criterion at convergence: 16837.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.6177 -0.6415 -0.2313  0.5060  7.9568 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.4027   0.6346  
##  Residual             0.6000   0.7746  
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)
## (Intercept) 1.342e-14  4.144e-02 2.450e+02   0.000    1.000
## scale(SE)   2.515e-02  4.144e-02 2.450e+02   0.607    0.545
## 
## Correlation of Fixed Effects:
##           (Intr)
## scale(SE) 0.000
m <-lmer(scale(idtSim) ~ scale(NFC) + ( 1 | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(NFC) + (1 | subID)
##    Data: idSim2
## 
## REML criterion at convergence: 16837.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.6158 -0.6429 -0.2305  0.5048  7.9553 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.4027   0.6346  
##  Residual             0.6000   0.7746  
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)
## (Intercept) 1.463e-14  4.144e-02 2.450e+02   0.000    1.000
## scale(NFC)  2.427e-02  4.144e-02 2.450e+02   0.586    0.559
## 
## Correlation of Fixed Effects:
##            (Intr)
## scale(NFC) 0.000
m <-lmer(scale(idtSim) ~ scale(DS) + ( 1 | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(DS) + (1 | subID)
##    Data: idSim2
## 
## REML criterion at convergence: 16835.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.6145 -0.6408 -0.2325  0.5041  7.9510 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.3991   0.6318  
##  Residual             0.6000   0.7746  
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)
## (Intercept) 1.239e-14  4.126e-02 2.450e+02   0.000    1.000
## scale(DS)   6.470e-02  4.127e-02 2.450e+02   1.568    0.118
## 
## Correlation of Fixed Effects:
##           (Intr)
## scale(DS) 0.000
m <-lmer(scale(idtSim) ~ scale(SCC) + ( 1| subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(SCC) + (1 | subID)
##    Data: idSim2
## 
## REML criterion at convergence: 16838.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.6165 -0.6416 -0.2314  0.5058  7.9550 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.4033   0.6350  
##  Residual             0.6000   0.7746  
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##               Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)  1.547e-14  4.147e-02  2.450e+02   0.000    1.000
## scale(SCC)  -9.042e-03  4.147e-02  2.450e+02  -0.218    0.828
## 
## Correlation of Fixed Effects:
##            (Intr)
## scale(SCC) 0.000
m <-lmer(scale(idtSim) ~ scale(MemSE) + ( 1 | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(MemSE) + (1 | subID)
##    Data: idSim2
## 
## REML criterion at convergence: 16837.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.6135 -0.6411 -0.2318  0.5057  7.9567 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.4019   0.6340  
##  Residual             0.6000   0.7746  
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##               Estimate Std. Error        df t value Pr(>|t|)
## (Intercept)  1.276e-14  4.140e-02 2.450e+02   0.000    1.000
## scale(MemSE) 3.735e-02  4.140e-02 2.450e+02   0.902    0.368
## 
## Correlation of Fixed Effects:
##             (Intr)
## scale(MmSE) 0.000
m <-lmer(scale(idtSim) ~ scale(PrivCSE) + ( 1 | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(PrivCSE) + (1 | subID)
##    Data: idSim2
## 
## REML criterion at convergence: 16835.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.6138 -0.6364 -0.2310  0.5063  7.9593 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.3982   0.6310  
##  Residual             0.6000   0.7746  
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##                 Estimate Std. Error        df t value Pr(>|t|)  
## (Intercept)    1.411e-14  4.122e-02 2.450e+02   0.000   1.0000  
## scale(PrivCSE) 7.145e-02  4.122e-02 2.450e+02   1.733   0.0843 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## scl(PrvCSE) 0.000
m <-lmer(scale(idtSim) ~ scale(PubCSE) + ( 1 | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(PubCSE) + (1 | subID)
##    Data: idSim2
## 
## REML criterion at convergence: 16838
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.6158 -0.6420 -0.2325  0.5049  7.9558 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.4029   0.6347  
##  Residual             0.6000   0.7746  
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##                Estimate Std. Error        df t value Pr(>|t|)
## (Intercept)   1.250e-14  4.145e-02 2.450e+02   0.000    1.000
## scale(PubCSE) 2.142e-02  4.145e-02 2.450e+02   0.517    0.606
## 
## Correlation of Fixed Effects:
##             (Intr)
## scal(PbCSE) 0.000
m <-lmer(scale(idtSim) ~ scale(MemSE) + ( 1 | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(MemSE) + (1 | subID)
##    Data: idSim2
## 
## REML criterion at convergence: 16837.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.6135 -0.6411 -0.2318  0.5057  7.9567 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.4019   0.6340  
##  Residual             0.6000   0.7746  
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##               Estimate Std. Error        df t value Pr(>|t|)
## (Intercept)  1.276e-14  4.140e-02 2.450e+02   0.000    1.000
## scale(MemSE) 3.735e-02  4.140e-02 2.450e+02   0.902    0.368
## 
## Correlation of Fixed Effects:
##             (Intr)
## scale(MmSE) 0.000

Individual Differences Predict Pairwise Overlap in Identities in Common

m <-lmer(scale(idSim) ~ scale(SCC) + ( 1 | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(SCC) + (1 | subID)
##    Data: idSim2
## 
## REML criterion at convergence: 17147.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3152 -0.6196 -0.2368  0.4783  3.8478 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.3708   0.6089  
##  Residual             0.6302   0.7939  
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##               Estimate Std. Error         df t value Pr(>|t|)
## (Intercept) -2.516e-14  3.990e-02  2.450e+02   0.000    1.000
## scale(SCC)  -4.540e-02  3.990e-02  2.450e+02  -1.138    0.256
## 
## Correlation of Fixed Effects:
##            (Intr)
## scale(SCC) 0.000
m <-lmer(scale(idSim) ~ scale(Ind) + ( 1 | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(Ind) + (1 | subID)
##    Data: idSim2
## 
## REML criterion at convergence: 17148.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3144 -0.6212 -0.2375  0.4812  3.8470 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.3716   0.6096  
##  Residual             0.6302   0.7939  
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##               Estimate Std. Error         df t value Pr(>|t|)
## (Intercept) -2.510e-14  3.995e-02  2.450e+02   0.000    1.000
## scale(Ind)  -3.467e-02  3.995e-02  2.450e+02  -0.868    0.386
## 
## Correlation of Fixed Effects:
##            (Intr)
## scale(Ind) 0.000
m <-lmer(scale(idSim) ~ scale(Inter) + ( 1 | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(Inter) + (1 | subID)
##    Data: idSim2
## 
## REML criterion at convergence: 17148.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3165 -0.6252 -0.2399  0.4817  3.8424 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.3728   0.6106  
##  Residual             0.6302   0.7939  
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##                Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)  -2.750e-14  4.001e-02  2.450e+02   0.000    1.000
## scale(Inter) -9.072e-04  4.001e-02  2.450e+02  -0.023    0.982
## 
## Correlation of Fixed Effects:
##             (Intr)
## scale(Intr) 0.000
m <-lmer(scale(idSim) ~ scale(SWLS) + ( 1 | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(SWLS) + (1 | subID)
##    Data: idSim2
## 
## REML criterion at convergence: 17148.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3162 -0.6289 -0.2364  0.4807  3.8476 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.3719   0.6098  
##  Residual             0.6302   0.7939  
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##               Estimate Std. Error         df t value Pr(>|t|)
## (Intercept) -2.929e-14  3.996e-02  2.450e+02   0.000    1.000
## scale(SWLS)  3.016e-02  3.996e-02  2.450e+02   0.755    0.451
## 
## Correlation of Fixed Effects:
##             (Intr)
## scale(SWLS) 0.000
m <-lmer(scale(idSim) ~ scale(IdImp) + ( 1 | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(IdImp) + (1 | subID)
##    Data: idSim2
## 
## REML criterion at convergence: 17148.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3145 -0.6296 -0.2388  0.4832  3.8486 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.3718   0.6098  
##  Residual             0.6302   0.7939  
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##                Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)  -2.759e-14  3.996e-02  2.450e+02   0.000    1.000
## scale(IdImp)  3.180e-02  3.996e-02  2.450e+02   0.796    0.427
## 
## Correlation of Fixed Effects:
##             (Intr)
## scal(IdImp) 0.000
m <-lmer(scale(idSim) ~ scale(phi) + ( 1 | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(phi) + (1 | subID)
##    Data: idSim2
## 
## REML criterion at convergence: 17144.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3162 -0.6263 -0.2362  0.4828  3.8520 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.3662   0.6052  
##  Residual             0.6302   0.7939  
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##               Estimate Std. Error         df t value Pr(>|t|)  
## (Intercept) -2.715e-14  3.967e-02  2.450e+02   0.000   1.0000  
## scale(phi)  -8.104e-02  3.967e-02  2.450e+02  -2.043   0.0422 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr)
## scale(phi) 0.000
m <-lmer(scale(idSim) ~ scale(overlap_norm) + ( 1 | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(overlap_norm) + (1 | subID)
##    Data: idSim2
## 
## REML criterion at convergence: 17124.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3312 -0.6329 -0.2323  0.4839  3.8549 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.3350   0.5788  
##  Residual             0.6302   0.7939  
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##                       Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)         -2.017e-14  3.804e-02  2.450e+02   0.000        1    
## scale(overlap_norm)  1.937e-01  3.805e-02  2.450e+02   5.092 7.08e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## scl(vrlp_n) 0.000
m <-lmer(scale(idSim) ~ scale(H_index) + ( 1 | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(H_index) + (1 | subID)
##    Data: idSim2
## 
## REML criterion at convergence: 17126.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3360 -0.6351 -0.2269  0.4837  3.8608 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.3390   0.5822  
##  Residual             0.6302   0.7939  
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)    -2.558e-14  3.825e-02  2.450e+02   0.000        1    
## scale(H_index)  1.833e-01  3.826e-02  2.450e+02   4.792 2.86e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## scal(H_ndx) 0.000
m <-lmer(scale(idSim) ~ scale(SE) + ( 1| subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(SE) + (1 | subID)
##    Data: idSim2
## 
## REML criterion at convergence: 17148.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3164 -0.6246 -0.2397  0.4815  3.8430 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.3728   0.6106  
##  Residual             0.6302   0.7939  
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##               Estimate Std. Error         df t value Pr(>|t|)
## (Intercept) -2.477e-14  4.001e-02  2.450e+02   0.000    1.000
## scale(SE)   -6.099e-03  4.001e-02  2.450e+02  -0.152    0.879
## 
## Correlation of Fixed Effects:
##           (Intr)
## scale(SE) 0.000
m <-lmer(scale(idSim) ~ scale(NFC) + ( 1 | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(NFC) + (1 | subID)
##    Data: idSim2
## 
## REML criterion at convergence: 17148.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3184 -0.6284 -0.2377  0.4783  3.8474 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.3718   0.6097  
##  Residual             0.6302   0.7939  
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##               Estimate Std. Error         df t value Pr(>|t|)
## (Intercept) -2.550e-14  3.995e-02  2.450e+02   0.000    1.000
## scale(NFC)  -3.243e-02  3.996e-02  2.450e+02  -0.812    0.418
## 
## Correlation of Fixed Effects:
##            (Intr)
## scale(NFC) 0.000
m <-lmer(scale(idSim) ~ scale(DS) + ( 1 | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(DS) + (1 | subID)
##    Data: idSim2
## 
## REML criterion at convergence: 17148.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3166 -0.6176 -0.2346  0.4801  3.8470 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.3716   0.6096  
##  Residual             0.6302   0.7939  
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##               Estimate Std. Error         df t value Pr(>|t|)
## (Intercept) -2.578e-14  3.994e-02  2.450e+02   0.000    1.000
## scale(DS)    3.498e-02  3.995e-02  2.450e+02   0.876    0.382
## 
## Correlation of Fixed Effects:
##           (Intr)
## scale(DS) 0.000
m <-lmer(scale(idSim) ~ scale(SCC) + ( 1| subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(SCC) + (1 | subID)
##    Data: idSim2
## 
## REML criterion at convergence: 17147.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3152 -0.6196 -0.2368  0.4783  3.8478 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.3708   0.6089  
##  Residual             0.6302   0.7939  
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##               Estimate Std. Error         df t value Pr(>|t|)
## (Intercept) -2.516e-14  3.990e-02  2.450e+02   0.000    1.000
## scale(SCC)  -4.540e-02  3.990e-02  2.450e+02  -1.138    0.256
## 
## Correlation of Fixed Effects:
##            (Intr)
## scale(SCC) 0.000
m <-lmer(scale(idSim) ~ scale(MemSE) + ( 1 | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(MemSE) + (1 | subID)
##    Data: idSim2
## 
## REML criterion at convergence: 17146.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3150 -0.6168 -0.2350  0.4811  3.8506 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.3695   0.6078  
##  Residual             0.6302   0.7939  
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##                Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)  -2.725e-14  3.984e-02  2.450e+02   0.000    1.000
## scale(MemSE) -5.771e-02  3.984e-02  2.450e+02  -1.448    0.149
## 
## Correlation of Fixed Effects:
##             (Intr)
## scale(MmSE) 0.000
m <-lmer(scale(idSim) ~ scale(PrivCSE) + ( 1 | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(PrivCSE) + (1 | subID)
##    Data: idSim2
## 
## REML criterion at convergence: 17144.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3200 -0.6124 -0.2308  0.4798  3.8547 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.3658   0.6048  
##  Residual             0.6302   0.7939  
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##                  Estimate Std. Error         df t value Pr(>|t|)  
## (Intercept)    -2.822e-14  3.965e-02  2.450e+02   0.000   1.0000  
## scale(PrivCSE) -8.376e-02  3.965e-02  2.450e+02  -2.113   0.0357 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## scl(PrvCSE) 0.000
m <-lmer(scale(idSim) ~ scale(PubCSE) + ( 1 | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(PubCSE) + (1 | subID)
##    Data: idSim2
## 
## REML criterion at convergence: 17148.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3164 -0.6223 -0.2388  0.4827  3.8453 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.3726   0.6104  
##  Residual             0.6302   0.7939  
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##                 Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)   -2.597e-14  3.999e-02  2.450e+02   0.000    1.000
## scale(PubCSE) -1.659e-02  4.000e-02  2.450e+02  -0.415    0.679
## 
## Correlation of Fixed Effects:
##             (Intr)
## scal(PbCSE) 0.000
m <-lmer(scale(idSim) ~ scale(MemSE) + ( 1 | subID), data=idSim2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(MemSE) + (1 | subID)
##    Data: idSim2
## 
## REML criterion at convergence: 17146.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3150 -0.6168 -0.2350  0.4811  3.8506 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.3695   0.6078  
##  Residual             0.6302   0.7939  
## Number of obs: 6916, groups:  subID, 247
## 
## Fixed effects:
##                Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)  -2.725e-14  3.984e-02  2.450e+02   0.000    1.000
## scale(MemSE) -5.771e-02  3.984e-02  2.450e+02  -1.448    0.149
## 
## Correlation of Fixed Effects:
##             (Intr)
## scale(MmSE) 0.000

More trait overlap predicts more intergroup bias

Study 2 Only

m<-lmer(scale(interG) ~ scale(traitCommNod) + scale(idCommNod) + ( scale(traitCommNod) + scale(idCommNod) | subID) + ( 1  | id), data=idShort2)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## scale(interG) ~ scale(traitCommNod) + scale(idCommNod) + (scale(traitCommNod) +  
##     scale(idCommNod) | subID) + (1 | id)
##    Data: idShort2
## 
## REML criterion at convergence: 3879.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.0433 -0.5218 -0.1128  0.4632  3.8308 
## 
## Random effects:
##  Groups   Name                Variance Std.Dev. Corr       
##  subID    (Intercept)         0.27073  0.52032             
##           scale(traitCommNod) 0.00535  0.07315   1.00      
##           scale(idCommNod)    0.01093  0.10454  -1.00 -1.00
##  id       (Intercept)         0.03795  0.19482             
##  Residual                     0.68072  0.82506             
## Number of obs: 1456, groups:  subID, 229; id, 8
## 
## Fixed effects:
##                      Estimate Std. Error        df t value Pr(>|t|)  
## (Intercept)          -0.01010    0.08042  10.68542  -0.126   0.9024  
## scale(traitCommNod)   0.10010    0.04370 189.51160   2.291   0.0231 *
## scale(idCommNod)     -0.07787    0.04035 564.23880  -1.930   0.0541 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(tCN)
## scl(trtCmN)  0.066         
## scl(dCmmNd) -0.117 -0.581  
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')

Identity Centrality Predicts Less Mutability

Study 1 Only

mall.I2I<-lmer(scale(mall) ~ scale(I2Ideg) + ( scale(I2Ideg) | subID) + ( 1  | id), data=idShort1)
## boundary (singular) fit: see help('isSingular')
summary(mall.I2I)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(mall) ~ scale(I2Ideg) + (scale(I2Ideg) | subID) + (1 |      id)
##    Data: idShort1
## 
## REML criterion at convergence: 5055.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2762 -0.6723  0.0482  0.6931  2.5704 
## 
## Random effects:
##  Groups   Name          Variance Std.Dev. Corr
##  subID    (Intercept)   0.136687 0.36971      
##           scale(I2Ideg) 0.002015 0.04489  1.00
##  id       (Intercept)   0.235380 0.48516      
##  Residual               0.662695 0.81406      
## Number of obs: 1968, groups:  subID, 246; id, 8
## 
## Fixed effects:
##                Estimate Std. Error        df t value Pr(>|t|)  
## (Intercept)   1.002e-03  1.741e-01 7.259e+00   0.006   0.9956  
## scale(I2Ideg) 5.031e-02  2.738e-02 1.930e+02   1.837   0.0677 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## scal(I2Idg) 0.022 
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')

Positivity of Identity Associated with Greater Intergroup Bias

Study 2 Only

More positive self-reported

m<-lmer(scale(interG) ~ scale(pos) + ( scale(pos) | subID) + ( 1  | id), data=idShort2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(interG) ~ scale(pos) + (scale(pos) | subID) + (1 | id)
##    Data: idShort2
## 
## REML criterion at convergence: 3755.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.0986 -0.5334 -0.1214  0.4823  3.5762 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr
##  subID    (Intercept) 0.27706  0.5264       
##           scale(pos)  0.04348  0.2085   0.34
##  id       (Intercept) 0.06469  0.2543       
##  Residual             0.58541  0.7651       
## Number of obs: 1456, groups:  subID, 229; id, 8
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)  -0.01950    0.09897   9.15112  -0.197    0.848    
## scale(pos)    0.26915    0.02943 217.33810   9.147   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr)
## scale(pos) 0.045

More positive traits associated with identity than negative

m<-lmer(scale(interG) ~ scale(pndiff) + ( scale(pndiff) | subID) + ( 1  | id), data=idShort2)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(interG) ~ scale(pndiff) + (scale(pndiff) | subID) + (1 |  
##     id)
##    Data: idShort2
## 
## REML criterion at convergence: 3858.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.6740 -0.5202 -0.1112  0.4929  3.8639 
## 
## Random effects:
##  Groups   Name          Variance Std.Dev. Corr
##  subID    (Intercept)   0.27711  0.5264       
##           scale(pndiff) 0.04091  0.2023   0.26
##  id       (Intercept)   0.03837  0.1959       
##  Residual               0.64979  0.8061       
## Number of obs: 1456, groups:  subID, 229; id, 8
## 
## Fixed effects:
##                Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)    0.008655   0.080989 10.832624   0.107    0.917    
## scale(pndiff)  0.195757   0.036648 66.891671   5.342 1.19e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## scal(pndff) 0.075

Differentiation and Inclusion Quadratic Association

Study 2 Only

m<-lmer(scale(differ) ~ scale(poly(inclus, 2)) + ( scale(inclus) | subID) + ( 1  | id), data=idShort2[!is.na(idShort2$differ) & !is.na(idShort2$inclus), ])
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(differ) ~ scale(poly(inclus, 2)) + (scale(inclus) | subID) +  
##     (1 | id)
##    Data: idShort2[!is.na(idShort2$differ) & !is.na(idShort2$inclus), ]
## 
## REML criterion at convergence: 5090.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.7915 -0.5418 -0.0094  0.6313  3.2085 
## 
## Random effects:
##  Groups   Name          Variance Std.Dev. Corr
##  subID    (Intercept)   0.21794  0.4668       
##           scale(inclus) 0.05309  0.2304   0.62
##  id       (Intercept)   0.02964  0.1722       
##  Residual               0.62675  0.7917       
## Number of obs: 1965, groups:  subID, 247; id, 8
## 
## Fixed effects:
##                           Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)              -0.004591   0.070413  10.429788  -0.065    0.949    
## scale(poly(inclus, 2))1   0.242719   0.026989 258.096574   8.993  < 2e-16 ***
## scale(poly(inclus, 2))2   0.091903   0.021812 755.472716   4.213 2.82e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) s((,2))1
## scl(p(,2))1 0.150          
## scl(p(,2))2 0.033  0.077
ggpredict(m, c(  "inclus" )) %>% plot(show.title=FALSE)
## Model contains splines or polynomial terms. Consider using `terms="inclus [all]"` to get smooth plots. See also package-vignette 'Marginal Effects at Specific Values'.

# Differentiation and Inclusion Interact in Predicting Identification

Study 2 Only

m<-lmer(scale(streng) ~ scale(inclus) * scale(differ) + ( scale(inclus) + scale(differ) | subID) + ( 1  | id), data=idShort2)
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(streng) ~ scale(inclus) * scale(differ) + (scale(inclus) +  
##     scale(differ) | subID) + (1 | id)
##    Data: idShort2
## 
## REML criterion at convergence: 4361.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -7.7156 -0.2872  0.0740  0.5601  3.3661 
## 
## Random effects:
##  Groups   Name          Variance Std.Dev. Corr       
##  subID    (Intercept)   0.087236 0.29536             
##           scale(inclus) 0.069104 0.26288  -1.00      
##           scale(differ) 0.001232 0.03511  -1.00  1.00
##  id       (Intercept)   0.092800 0.30463             
##  Residual               0.453181 0.67319             
## Number of obs: 1965, groups:  subID, 247; id, 8
## 
## Fixed effects:
##                               Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)                    0.03494    0.11066    7.44799   0.316 0.760879
## scale(inclus)                  0.39340    0.02612  321.79456  15.058  < 2e-16
## scale(differ)                  0.09759    0.01838 1371.69049   5.308 1.29e-07
## scale(inclus):scale(differ)   -0.06436    0.01770 1776.81985  -3.637 0.000284
##                                
## (Intercept)                    
## scale(inclus)               ***
## scale(differ)               ***
## scale(inclus):scale(differ) ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(n) scl(d)
## scale(ncls) -0.134              
## scale(dffr) -0.013 -0.085       
## scl(ncl):() -0.026  0.013 -0.357
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
ggpredict(m, c(  "inclus" , "differ")) %>% plot(show.title=FALSE)

m<-lmer(scale(streng) ~ scale(poly(inclus, 2)) + scale(poly(differ, 2)) + ( scale(poly(inclus, 2)) + scale(poly(differ, 2)) | subID) + ( 1  | id), data=idShort2[!is.na(idShort2$differ) & !is.na(idShort2$inclus), ])
## boundary (singular) fit: see help('isSingular')
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(streng) ~ scale(poly(inclus, 2)) + scale(poly(differ, 2)) +  
##     (scale(poly(inclus, 2)) + scale(poly(differ, 2)) | subID) +      (1 | id)
##    Data: idShort2[!is.na(idShort2$differ) & !is.na(idShort2$inclus), ]
## 
## REML criterion at convergence: 4352.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -7.8400 -0.3756  0.0737  0.5286  3.4453 
## 
## Random effects:
##  Groups   Name                    Variance Std.Dev. Corr                   
##  subID    (Intercept)             0.093505 0.30579                         
##           scale(poly(inclus, 2))1 0.069524 0.26367  -0.89                  
##           scale(poly(inclus, 2))2 0.018036 0.13430  -0.15 -0.32            
##           scale(poly(differ, 2))1 0.001403 0.03745  -1.00  0.93  0.06      
##           scale(poly(differ, 2))2 0.000141 0.01188   0.87 -0.55 -0.62 -0.82
##  id       (Intercept)             0.093977 0.30656                         
##  Residual                         0.435159 0.65967                         
## Number of obs: 1965, groups:  subID, 247; id, 8
## 
## Fixed effects:
##                          Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)               0.01446    0.11144   7.47900   0.130  0.90021    
## scale(poly(inclus, 2))1   0.39923    0.02785 214.68616  14.336  < 2e-16 ***
## scale(poly(inclus, 2))2  -0.03643    0.02304 165.16520  -1.581  0.11578    
## scale(poly(differ, 2))1   0.07732    0.01751 548.65839   4.417 1.21e-05 ***
## scale(poly(differ, 2))2   0.04949    0.01716 244.45174   2.885  0.00427 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##                (Intr) scl(ply(n,2))1 scl(ply(n,2))2 scl(ply(d,2))1
## scl(ply(n,2))1 -0.129                                             
## scl(ply(n,2))2  0.015 -0.298                                      
## scl(ply(d,2))1 -0.026 -0.088         -0.052                       
## scl(ply(d,2))2  0.007 -0.135         -0.110         -0.003        
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
ggpredict(m, c(  "inclus[all]" , "differ[all]")) %>% plot(show.title=FALSE)

Network Measures Correlated with External Measures

attempted in tidyverse

# networkMeasures %>% map(~ .x) %>% select(indDiff1, SE:CESD, .)
# 
# selected<-map(networkMeasures, ~ select(indDiff1, SE:CESD, .) )
# map2(networkMeasures, ~ selected, networkMeasures )

Study 1

networkMeasures <- indDiff1 %>% select(matches("H_index"):matches("globEff")) %>% colnames(.)

for(i in 1:length(networkMeasures)){
  corsDf <- indDiff1 %>% select(SE:CESD, networkMeasures[i] ) %>% corToOne(., networkMeasures[i])
  barsDf <- indDiff1 %>% select(SE:CESD, networkMeasures[i] ) %>% plotCorToOne(., networkMeasures[i])
  
  assign(paste0(networkMeasures[i],".CorDf"),corsDf)
  assign(paste0(networkMeasures[i],".CorPlotDf"),barsDf)
  
  print(corsDf)
  print(barsDf)
}
## Loading required package: tidyverse
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
## ✔ tibble  3.1.7     ✔ purrr   0.3.4
## ✔ tidyr   1.2.0     ✔ stringr 1.4.0
## ✔ readr   2.1.1     ✔ forcats 0.5.0
## Warning: package 'tidyr' was built under R version 4.0.5
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ tidyr::expand() masks Matrix::expand()
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ✖ tidyr::pack()   masks Matrix::pack()
## ✖ tidyr::unpack() masks Matrix::unpack()
## Loading required package: corrr
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## Note: Using an external vector in selections is ambiguous.
## ℹ Use `all_of(referenceVar)` instead of `referenceVar` to silence this message.
## ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## # A tibble: 12 × 2
##    Variables   H_index
##    <chr>         <dbl>
##  1 SCC       -0.0665  
##  2 SE        -0.0168  
##  3 IdImp     -0.00887 
##  4 SWLS      -0.000116
##  5 MemSE      0.00559 
##  6 PrivCSE    0.0118  
##  7 PubCSE     0.0195  
##  8 DS         0.0406  
##  9 CESD       0.0855  
## 10 NFC        0.127   
## 11 Inter      0.148   
## 12 Ind        0.190
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

## # A tibble: 12 × 2
##    Variables overlap_norm
##    <chr>            <dbl>
##  1 DS             -0.101 
##  2 CESD           -0.0496
##  3 IdImp           0.0363
##  4 SCC             0.0596
##  5 SE              0.0813
##  6 SWLS            0.0992
##  7 MemSE           0.103 
##  8 PrivCSE         0.154 
##  9 Inter           0.177 
## 10 PubCSE          0.177 
## 11 NFC             0.197 
## 12 Ind             0.248
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

## # A tibble: 12 × 2
##    Variables     phi
##    <chr>       <dbl>
##  1 SWLS      -0.218 
##  2 NFC       -0.153 
##  3 SE        -0.148 
##  4 SCC       -0.0798
##  5 MemSE     -0.0766
##  6 PubCSE    -0.0722
##  7 Ind       -0.0528
##  8 Inter     -0.0318
##  9 PrivCSE   -0.0125
## 10 IdImp      0.0158
## 11 DS         0.0164
## 12 CESD       0.0764
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

## # A tibble: 12 × 2
##    Variables   idEdgT
##    <chr>        <dbl>
##  1 SE        -0.0571 
##  2 Ind       -0.0512 
##  3 SCC       -0.0301 
##  4 DS        -0.00381
##  5 SWLS       0.00368
##  6 MemSE      0.00383
##  7 Inter      0.0213 
##  8 PubCSE     0.0221 
##  9 NFC        0.0255 
## 10 PrivCSE    0.0328 
## 11 IdImp      0.0689 
## 12 CESD       0.0844
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

## # A tibble: 12 × 2
##    Variables idDensity
##    <chr>         <dbl>
##  1 SE         -0.0571 
##  2 Ind        -0.0512 
##  3 SCC        -0.0301 
##  4 DS         -0.00381
##  5 SWLS        0.00368
##  6 MemSE       0.00383
##  7 Inter       0.0213 
##  8 PubCSE      0.0221 
##  9 NFC         0.0255 
## 10 PrivCSE     0.0328 
## 11 IdImp       0.0689 
## 12 CESD        0.0844
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

## # A tibble: 12 × 2
##    Variables  idTrans
##    <chr>        <dbl>
##  1 SE        -0.0547 
##  2 DS        -0.0443 
##  3 PubCSE    -0.0417 
##  4 Inter     -0.0256 
##  5 SCC       -0.0206 
##  6 MemSE     -0.0181 
##  7 PrivCSE   -0.0112 
##  8 Ind       -0.00978
##  9 SWLS       0.0127 
## 10 CESD       0.0551 
## 11 IdImp      0.0646 
## 12 NFC        0.0687
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

## # A tibble: 12 × 2
##    Variables    idSW
##    <chr>       <dbl>
##  1 PubCSE    -0.0533
##  2 SE        -0.0436
##  3 Inter     -0.0207
##  4 SCC       -0.0148
##  5 DS        -0.0114
##  6 MemSE      0.0306
##  7 CESD       0.0392
##  8 PrivCSE    0.0419
##  9 IdImp      0.0568
## 10 SWLS       0.0685
## 11 Ind        0.0770
## 12 NFC        0.121
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

## # A tibble: 12 × 2
##    Variables idStrengHom
##    <chr>           <dbl>
##  1 SWLS         -0.0444 
##  2 DS           -0.0328 
##  3 SCC          -0.00631
##  4 IdImp         0.0159 
##  5 SE            0.0242 
##  6 Inter         0.0466 
##  7 NFC           0.0485 
##  8 PrivCSE       0.0647 
##  9 CESD          0.0837 
## 10 PubCSE        0.0998 
## 11 MemSE         0.102  
## 12 Ind           0.110
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

## # A tibble: 12 × 2
##    Variables idPosHom
##    <chr>        <dbl>
##  1 PubCSE     -0.0486
##  2 SCC        -0.0443
##  3 PrivCSE    -0.0437
##  4 Ind        -0.0239
##  5 NFC        -0.0149
##  6 MemSE      -0.0106
##  7 Inter       0.0226
##  8 SE          0.0246
##  9 IdImp       0.0320
## 10 DS          0.0387
## 11 CESD        0.0608
## 12 SWLS        0.143
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

## # A tibble: 12 × 2
##    Variables idUnifHom
##    <chr>         <dbl>
##  1 SCC        -0.110  
##  2 PubCSE     -0.105  
##  3 SE         -0.0351 
##  4 MemSE       0.00428
##  5 SWLS        0.0544 
##  6 Ind         0.0569 
##  7 PrivCSE     0.0584 
##  8 Inter       0.0847 
##  9 NFC         0.0918 
## 10 IdImp       0.0927 
## 11 DS          0.107  
## 12 CESD        0.122
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

## # A tibble: 12 × 2
##    Variables idMallHom
##    <chr>         <dbl>
##  1 PrivCSE   -0.0958  
##  2 MemSE     -0.0810  
##  3 SCC       -0.0579  
##  4 Ind       -0.0525  
##  5 IdImp     -0.0414  
##  6 PubCSE    -0.0355  
##  7 Inter     -0.0143  
##  8 SE        -0.00409 
##  9 DS        -0.000136
## 10 SWLS       0.0579  
## 11 NFC        0.0731  
## 12 CESD       0.101
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

## # A tibble: 12 × 2
##    Variables idCommGlob
##    <chr>          <dbl>
##  1 DS          -0.0605 
##  2 SE          -0.0236 
##  3 Ind         -0.0145 
##  4 SCC         -0.00177
##  5 PubCSE       0.00298
##  6 MemSE        0.00876
##  7 SWLS         0.0186 
##  8 NFC          0.0491 
##  9 Inter        0.0500 
## 10 PrivCSE      0.0646 
## 11 CESD         0.0792 
## 12 IdImp        0.0889
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

## # A tibble: 12 × 2
##    Variables traitCommGlob
##    <chr>             <dbl>
##  1 DS              -0.0808
##  2 CESD            -0.0164
##  3 SCC              0.0330
##  4 IdImp            0.0364
##  5 SE               0.0735
##  6 MemSE            0.0755
##  7 SWLS             0.103 
##  8 Inter            0.125 
##  9 Ind              0.126 
## 10 PrivCSE          0.132 
## 11 NFC              0.150 
## 12 PubCSE           0.173
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

## # A tibble: 12 × 2
##    Variables  globEff
##    <chr>        <dbl>
##  1 SCC       -0.0494 
##  2 SE        -0.0366 
##  3 PubCSE    -0.0330 
##  4 MemSE     -0.00756
##  5 DS        -0.00580
##  6 Ind       -0.00291
##  7 SWLS       0.00570
##  8 PrivCSE    0.0418 
##  9 Inter      0.0552 
## 10 NFC        0.0748 
## 11 IdImp      0.107  
## 12 CESD       0.131

Study 2

networkMeasures <- indDiff2 %>% select(matches("H_index"):matches("globEff")) %>% colnames(.)

for(i in 1:length(networkMeasures)){
  corsDf <- indDiff2 %>% select(SE:MC, networkMeasures[i] ) %>% corToOne(., networkMeasures[i])
  barsDf <- indDiff2 %>% select(SE:MC, networkMeasures[i] ) %>% plotCorToOne(., networkMeasures[i])
  
  assign(paste0(networkMeasures[i],".CorDf"),corsDf)
  assign(paste0(networkMeasures[i],".CorPlotDf"),barsDf)
  
  print(corsDf)
  print(barsDf)
}
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## # A tibble: 15 × 2
##    Variables H_index
##    <chr>       <dbl>
##  1 MC        -0.141 
##  2 EMS       -0.105 
##  3 SCC       -0.0928
##  4 SWLS      -0.0840
##  5 SE        -0.0640
##  6 NFC        0.0349
##  7 Ind        0.0364
##  8 PubCSE     0.0553
##  9 Inter      0.0564
## 10 IdImp      0.0597
## 11 CESD       0.0689
## 12 MemSE      0.0694
## 13 PrivCSE    0.0873
## 14 DS         0.118 
## 15 IMS        0.127
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

## # A tibble: 15 × 2
##    Variables overlap_norm
##    <chr>            <dbl>
##  1 EMS            -0.126 
##  2 MC             -0.0700
##  3 SWLS           -0.0334
##  4 CESD           -0.0127
##  5 SCC            -0.0117
##  6 Ind             0.0402
##  7 SE              0.0414
##  8 PubCSE          0.0442
##  9 MemSE           0.0630
## 10 NFC             0.0723
## 11 DS              0.0752
## 12 IdImp           0.0770
## 13 IMS             0.0800
## 14 Inter           0.0821
## 15 PrivCSE         0.122
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

## # A tibble: 15 × 2
##    Variables      phi
##    <chr>        <dbl>
##  1 IdImp     -0.136  
##  2 EMS       -0.114  
##  3 PrivCSE   -0.0621 
##  4 SCC       -0.0322 
##  5 PubCSE    -0.0321 
##  6 CESD      -0.0221 
##  7 MemSE     -0.0205 
##  8 Ind       -0.00707
##  9 DS         0.00926
## 10 IMS        0.0112 
## 11 MC         0.0162 
## 12 Inter      0.0311 
## 13 SWLS       0.0413 
## 14 NFC        0.0715 
## 15 SE         0.100
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

## # A tibble: 15 × 2
##    Variables   idEdgT
##    <chr>        <dbl>
##  1 PrivCSE   -0.115  
##  2 SCC       -0.0786 
##  3 MemSE     -0.0741 
##  4 MC        -0.0699 
##  5 IMS       -0.0551 
##  6 NFC       -0.0444 
##  7 Ind       -0.0442 
##  8 PubCSE    -0.0109 
##  9 SE        -0.00581
## 10 Inter      0.0185 
## 11 DS         0.0317 
## 12 EMS        0.0400 
## 13 SWLS       0.0512 
## 14 IdImp      0.0690 
## 15 CESD       0.0826
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

## # A tibble: 15 × 2
##    Variables idDensity
##    <chr>         <dbl>
##  1 PrivCSE    -0.115  
##  2 SCC        -0.0786 
##  3 MemSE      -0.0741 
##  4 MC         -0.0699 
##  5 IMS        -0.0551 
##  6 NFC        -0.0444 
##  7 Ind        -0.0442 
##  8 PubCSE     -0.0109 
##  9 SE         -0.00581
## 10 Inter       0.0185 
## 11 DS          0.0317 
## 12 EMS         0.0400 
## 13 SWLS        0.0512 
## 14 IdImp       0.0690 
## 15 CESD        0.0826
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

## # A tibble: 15 × 2
##    Variables  idTrans
##    <chr>        <dbl>
##  1 IMS       -0.106  
##  2 MemSE     -0.0365 
##  3 Ind       -0.0294 
##  4 PrivCSE   -0.0289 
##  5 SCC       -0.0268 
##  6 NFC       -0.0128 
##  7 MC        -0.00445
##  8 PubCSE    -0.00337
##  9 Inter      0.00773
## 10 SE         0.0104 
## 11 DS         0.0128 
## 12 SWLS       0.0570 
## 13 CESD       0.0660 
## 14 EMS        0.0824 
## 15 IdImp      0.111
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

## # A tibble: 15 × 2
##    Variables     idSW
##    <chr>        <dbl>
##  1 IMS       -0.0970 
##  2 PubCSE    -0.0521 
##  3 CESD      -0.0493 
##  4 EMS       -0.0396 
##  5 Inter     -0.0152 
##  6 DS        -0.0117 
##  7 SWLS       0.00493
##  8 NFC        0.0137 
##  9 PrivCSE    0.0183 
## 10 MemSE      0.0214 
## 11 SE         0.0296 
## 12 MC         0.0428 
## 13 IdImp      0.0432 
## 14 Ind        0.0557 
## 15 SCC        0.102
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

## # A tibble: 15 × 2
##    Variables idStrengHom
##    <chr>           <dbl>
##  1 DS            -0.0805
##  2 CESD          -0.0704
##  3 MC            -0.0518
##  4 NFC           -0.0398
##  5 Inter         -0.0168
##  6 EMS            0.0142
##  7 Ind            0.0377
##  8 PrivCSE        0.0378
##  9 IdImp          0.0391
## 10 PubCSE         0.0395
## 11 SCC            0.0441
## 12 MemSE          0.0532
## 13 IMS            0.0613
## 14 SE             0.0841
## 15 SWLS           0.129
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

## # A tibble: 15 × 2
##    Variables idPosHom
##    <chr>        <dbl>
##  1 CESD      -0.0837 
##  2 PubCSE    -0.0741 
##  3 MemSE     -0.0672 
##  4 DS        -0.0666 
##  5 NFC       -0.0664 
##  6 IMS       -0.0495 
##  7 MC        -0.0384 
##  8 Inter     -0.0302 
##  9 Ind       -0.0213 
## 10 PrivCSE   -0.0114 
## 11 IdImp      0.00715
## 12 SCC        0.0285 
## 13 SE         0.0674 
## 14 EMS        0.0776 
## 15 SWLS       0.0826
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

## # A tibble: 15 × 2
##    Variables idStigHom
##    <chr>         <dbl>
##  1 EMS        -0.105  
##  2 SWLS       -0.0594 
##  3 PrivCSE    -0.0387 
##  4 MemSE      -0.0387 
##  5 IdImp      -0.0368 
##  6 Ind        -0.0290 
##  7 DS         -0.0119 
##  8 PubCSE     -0.0114 
##  9 Inter      -0.0111 
## 10 MC         -0.0104 
## 11 SE          0.00195
## 12 SCC         0.00535
## 13 IMS         0.0323 
## 14 CESD        0.0383 
## 15 NFC         0.0943
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

## # A tibble: 15 × 2
##    Variables idSizeSHom
##    <chr>          <dbl>
##  1 MC          -0.0712 
##  2 EMS         -0.0625 
##  3 Ind         -0.0589 
##  4 DS          -0.0428 
##  5 CESD        -0.0403 
##  6 SWLS        -0.0359 
##  7 SE          -0.0324 
##  8 PrivCSE     -0.0269 
##  9 NFC         -0.00678
## 10 Inter        0.0236 
## 11 SCC          0.0430 
## 12 MemSE        0.0603 
## 13 IdImp        0.112  
## 14 PubCSE       0.113  
## 15 IMS          0.120
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

## # A tibble: 15 × 2
##    Variables idSizeUHom
##    <chr>          <dbl>
##  1 SWLS        -0.0964 
##  2 EMS         -0.0616 
##  3 NFC         -0.0494 
##  4 IdImp       -0.0414 
##  5 IMS         -0.0293 
##  6 Inter       -0.0226 
##  7 MC          -0.0136 
##  8 Ind         -0.00966
##  9 SE          -0.00218
## 10 DS           0.00616
## 11 PubCSE       0.0170 
## 12 CESD         0.0268 
## 13 PrivCSE      0.0384 
## 14 SCC          0.0656 
## 15 MemSE        0.0813
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

## # A tibble: 15 × 2
##    Variables idSizeDHom
##    <chr>          <dbl>
##  1 MC           -0.132 
##  2 Ind          -0.102 
##  3 NFC          -0.0959
##  4 CESD         -0.0805
##  5 EMS          -0.0500
##  6 MemSE        -0.0157
##  7 PrivCSE      -0.0120
##  8 IdImp         0.0158
##  9 Inter         0.0183
## 10 SWLS          0.0239
## 11 PubCSE        0.0343
## 12 DS            0.0432
## 13 SE            0.0439
## 14 SCC           0.0463
## 15 IMS           0.0621
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

## # A tibble: 15 × 2
##    Variables idInclusHom
##    <chr>           <dbl>
##  1 SE          -0.107   
##  2 SCC         -0.0977  
##  3 NFC         -0.0933  
##  4 EMS         -0.0733  
##  5 MemSE       -0.0284  
##  6 MC          -0.0119  
##  7 IdImp        0.000756
##  8 PrivCSE      0.000878
##  9 DS           0.00376 
## 10 Ind          0.0154  
## 11 PubCSE       0.0203  
## 12 SWLS         0.0472  
## 13 CESD         0.0474  
## 14 Inter        0.0591  
## 15 IMS          0.0734
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

## # A tibble: 15 × 2
##    Variables idDifferHom
##    <chr>           <dbl>
##  1 MemSE        -0.164  
##  2 MC           -0.126  
##  3 IMS          -0.0976 
##  4 SE           -0.0863 
##  5 Ind          -0.0820 
##  6 Inter        -0.0746 
##  7 NFC          -0.0653 
##  8 PubCSE       -0.0638 
##  9 PrivCSE      -0.0430 
## 10 CESD         -0.0220 
## 11 SWLS         -0.00157
## 12 IdImp         0.0144 
## 13 EMS           0.0147 
## 14 SCC           0.0391 
## 15 DS            0.0681
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

## # A tibble: 15 × 2
##    Variables idInbHom
##    <chr>        <dbl>
##  1 SE        -0.128  
##  2 MC        -0.122  
##  3 SCC       -0.0657 
##  4 Ind       -0.0657 
##  5 MemSE     -0.0405 
##  6 NFC       -0.0377 
##  7 DS        -0.0210 
##  8 Inter     -0.00675
##  9 EMS        0.00403
## 10 SWLS       0.0151 
## 11 IMS        0.0341 
## 12 PrivCSE    0.0437 
## 13 PubCSE     0.0592 
## 14 CESD       0.0598 
## 15 IdImp      0.0676
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

## # A tibble: 15 × 2
##    Variables idOutbHom
##    <chr>         <dbl>
##  1 IdImp     -0.116   
##  2 MemSE     -0.104   
##  3 SCC       -0.0885  
##  4 PrivCSE   -0.0857  
##  5 PubCSE    -0.0339  
##  6 Ind       -0.0238  
##  7 MC        -0.0202  
##  8 CESD      -0.0170  
##  9 IMS       -0.00583 
## 10 SE        -0.00181 
## 11 Inter     -0.00169 
## 12 NFC       -0.000641
## 13 EMS        0.0435  
## 14 SWLS       0.0494  
## 15 DS         0.104
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

## # A tibble: 15 × 2
##    Variables idInterbHom
##    <chr>           <dbl>
##  1 MemSE        -0.178  
##  2 MC           -0.122  
##  3 PrivCSE      -0.121  
##  4 IdImp        -0.0729 
##  5 SCC          -0.0723 
##  6 SE           -0.0644 
##  7 Ind          -0.0321 
##  8 SWLS         -0.0126 
##  9 EMS           0.00285
## 10 IMS           0.0120 
## 11 PubCSE        0.0301 
## 12 NFC           0.0315 
## 13 CESD          0.0439 
## 14 Inter         0.0561 
## 15 DS            0.0690
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

## # A tibble: 15 × 2
##    Variables traitCommGlob
##    <chr>             <dbl>
##  1 MC             -0.0630 
##  2 IMS            -0.0195 
##  3 SWLS           -0.0179 
##  4 Ind            -0.0135 
##  5 SCC            -0.00824
##  6 EMS            -0.00396
##  7 PubCSE          0.00558
##  8 MemSE           0.0171 
##  9 NFC             0.0222 
## 10 PrivCSE         0.0240 
## 11 CESD            0.0321 
## 12 Inter           0.0338 
## 13 SE              0.0395 
## 14 DS              0.0773 
## 15 IdImp           0.112
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

## # A tibble: 15 × 2
##    Variables idCommGlob
##    <chr>          <dbl>
##  1 PrivCSE     -0.134  
##  2 IMS         -0.129  
##  3 MemSE       -0.0921 
##  4 SCC         -0.0725 
##  5 MC          -0.0593 
##  6 Ind         -0.0554 
##  7 NFC         -0.0518 
##  8 PubCSE      -0.0265 
##  9 SE          -0.00974
## 10 Inter       -0.00145
## 11 SWLS         0.0482 
## 12 IdImp        0.0508 
## 13 DS           0.0559 
## 14 CESD         0.104  
## 15 EMS          0.107
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## [1] "All required packages attached"
## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

## # A tibble: 15 × 2
##    Variables globEff
##    <chr>       <dbl>
##  1 MC        -0.126 
##  2 PrivCSE   -0.124 
##  3 IMS       -0.120 
##  4 SCC       -0.116 
##  5 MemSE     -0.104 
##  6 NFC       -0.0686
##  7 SE        -0.0523
##  8 PubCSE    -0.0465
##  9 Ind       -0.0334
## 10 Inter      0.0203
## 11 SWLS       0.0347
## 12 DS         0.0629
## 13 IdImp      0.0667
## 14 EMS        0.128 
## 15 CESD       0.153

Normative Latino Ratings

normTraits <- read.csv("~/Google Drive/Volumes/Research Project/Identities to Traits/Study 2/Cleaning/Output/Normative/normativeDfStudy12.csv", header = T)

fullLong1s <- fullLong1 %>% select(subID,id,idC,connect,traits,selfResp,IdIn,connect,streng)
fullLong2s <- fullLong2 %>% select(subID,id,idC,connect,traits,selfResp,IdIn,connect,streng)
fullLong2s$subID <- fullLong2$subID + 100000

combLong <- rbind(fullLong1s,fullLong2s)

combLong <- merge(combLong, normTraits, by = "traits")
combLong$ynLatin <- ifelse(combLong$idC==2 & combLong$id=="Race", "HL", "Not HL")
combLong$ynAsian <- ifelse(combLong$idC==4 & combLong$id=="Race", "As", "Not As")
combLong$ynMale <- ifelse(combLong$idC==1 & combLong$id=="Gen", "M", "Not M")
combLong$ynFemale <- ifelse(combLong$idC==2 & combLong$id=="Gen", "F", "Not F")
combLong$ynHetero <- ifelse(combLong$idC==1 & combLong$id=="Sex", "Het", "Not Het")
combLong$ynBis <- ifelse(combLong$idC==3 & combLong$id=="Sex", "Bi", "Not Bi")
combLong$ynCath <- ifelse(combLong$idC==1 & combLong$id=="Rel", "Cath", "Not Cath")
combLong$ynChrist <- ifelse(combLong$idC==2 & combLong$id=="Rel", "Christ", "Not Christ")
combLong$ynAgnos <- ifelse(combLong$idC==8 & combLong$id=="Rel", "Agn", "Not Agn")
combLong$ynAthei <- ifelse(combLong$idC==9 & combLong$id=="Rel", "Ath", "Not Ath")
combLong$ynDem <- ifelse(combLong$idC==1 & combLong$id=="Pol", "Dem", "Not Dem")
raceLong <- subset(combLong, id == "Race")
m <- lmer( scale(selfResp) ~ scale(Latino)*ynLatin + ( scale(Latino) | subID) + ( 1 | traits), data = raceLong)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00279822 (tol = 0.002, component 1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(selfResp) ~ scale(Latino) * ynLatin + (scale(Latino) |  
##     subID) + (1 | traits)
##    Data: raceLong
## 
## REML criterion at convergence: 349190.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.8241 -0.6639 -0.0196  0.6543  4.1272 
## 
## Random effects:
##  Groups   Name          Variance Std.Dev. Corr 
##  subID    (Intercept)   0.04726  0.2174        
##           scale(Latino) 0.03144  0.1773   -0.13
##  traits   (Intercept)   0.22170  0.4708        
##  Residual               0.61602  0.7849        
## Number of obs: 146472, groups:  subID, 495; traits, 296
## 
## Fixed effects:
##                              Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)                  -0.03636    0.03240 521.62068  -1.122  0.26234    
## scale(Latino)                 0.32761    0.03088 451.25397  10.610  < 2e-16 ***
## ynLatinNot HL                 0.05416    0.02121 492.98224   2.553  0.01098 *  
## scale(Latino):ynLatinNot HL  -0.05624    0.01748 492.92993  -3.217  0.00138 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(L) ynLNHL
## scale(Latn) -0.030              
## ynLatinNtHL -0.438  0.045       
## scl(L):LNHL  0.052 -0.379 -0.120
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00279822 (tol = 0.002, component 1)
ggpredict(m, c("Latino", "ynLatin")) %>% plot()

m <- lmer( scale(selfResp) ~ scale(Asian)*ynAsian + ( scale(Asian) | subID) + ( 1 | traits), data = raceLong)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00221085 (tol = 0.002, component 1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(selfResp) ~ scale(Asian) * ynAsian + (scale(Asian) | subID) +  
##     (1 | traits)
##    Data: raceLong
## 
## REML criterion at convergence: 348652.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.1844 -0.6590 -0.0212  0.6499  4.6725 
## 
## Random effects:
##  Groups   Name         Variance Std.Dev. Corr 
##  subID    (Intercept)  0.04782  0.2187        
##           scale(Asian) 0.03129  0.1769   -0.20
##  traits   (Intercept)  0.20100  0.4483        
##  Residual              0.61389  0.7835        
## Number of obs: 146472, groups:  subID, 495; traits, 296
## 
## Fixed effects:
##                             Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)                  0.01214    0.03064 511.00512   0.396    0.692    
## scale(Asian)                 0.25521    0.02920 440.93320   8.739  < 2e-16 ***
## ynAsianNot As               -0.02005    0.02060 492.96482  -0.973    0.331    
## scale(Asian):ynAsianNot As   0.11168    0.01685 492.97887   6.629 8.92e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(A) ynAsNA
## scale(Asin) -0.045              
## ynAsianNtAs -0.412  0.068       
## scl(As):ANA  0.079 -0.353 -0.191
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00221085 (tol = 0.002, component 1)
ggpredict(m, c("Asian", "ynAsian")) %>% plot()

m <- lmer( scale(selfResp) ~ scale(Asian)*ynAsian*streng + ( scale(Asian) | subID) + ( 1 | traits), data = raceLong)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00416783 (tol = 0.002, component 1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(selfResp) ~ scale(Asian) * ynAsian * streng + (scale(Asian) |  
##     subID) + (1 | traits)
##    Data: raceLong
## 
## REML criterion at convergence: 348638.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.1822 -0.6591 -0.0213  0.6496  4.6485 
## 
## Random effects:
##  Groups   Name         Variance Std.Dev. Corr 
##  subID    (Intercept)  0.04754  0.2180        
##           scale(Asian) 0.02915  0.1707   -0.20
##  traits   (Intercept)  0.20097  0.4483        
##  Residual              0.61389  0.7835        
## Number of obs: 146472, groups:  subID, 495; traits, 296
## 
## Fixed effects:
##                                    Estimate Std. Error        df t value
## (Intercept)                         0.26323    0.12290 536.27489   2.142
## scale(Asian)                       -0.01058    0.09878 561.73559  -0.107
## ynAsianNot As                      -0.33284    0.16712 490.98978  -1.992
## streng                             -0.03752    0.01778 490.97799  -2.110
## scale(Asian):ynAsianNot As         -0.10563    0.13258 490.90766  -0.797
## scale(Asian):streng                 0.03971    0.01411 490.88169   2.815
## ynAsianNot As:streng                0.04669    0.02472 490.98999   1.889
## scale(Asian):ynAsianNot As:streng   0.03211    0.01961 490.90845   1.637
##                                   Pr(>|t|)   
## (Intercept)                        0.03266 * 
## scale(Asian)                       0.91478   
## ynAsianNot As                      0.04697 * 
## streng                             0.03540 * 
## scale(Asian):ynAsianNot As         0.42603   
## scale(Asian):streng                0.00508 **
## ynAsianNot As:streng               0.05953 . 
## scale(Asian):ynAsianNot As:streng  0.10222   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(A) ynAsNA streng sc(A):ANA sc(A): ynANA:
## scale(Asin) -0.182                                             
## ynAsianNtAs -0.702  0.134                                      
## streng      -0.968  0.184  0.712                               
## scl(As):ANA  0.136 -0.693 -0.193 -0.137                        
## scl(Asn):st  0.187 -0.956 -0.137 -0.193  0.712                 
## ynAsnNtAs:s  0.697 -0.133 -0.992 -0.719  0.191     0.139       
## scl(A):ANA: -0.134  0.688  0.191  0.139 -0.992    -0.719 -0.193
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00416783 (tol = 0.002, component 1)
ggpredict(m, c("Asian", "streng")) %>% plot()

GenLong <- subset(combLong, id=="Gen")
m <- lmer( scale(selfResp) ~ scale(Female)*ynFemale + ( scale(Female) | subID) + ( 1 | traits), data = GenLong)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00218099 (tol = 0.002, component 1)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(selfResp) ~ scale(Female) * ynFemale + (scale(Female) |  
##     subID) + (1 | traits)
##    Data: GenLong
## 
## REML criterion at convergence: 344504.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.8983 -0.6503 -0.0183  0.6421  4.3392 
## 
## Random effects:
##  Groups   Name          Variance Std.Dev. Corr 
##  subID    (Intercept)   0.04796  0.2190        
##           scale(Female) 0.04941  0.2223   -0.23
##  traits   (Intercept)   0.14363  0.3790        
##  Residual               0.59616  0.7721        
## Number of obs: 146472, groups:  subID, 495; traits, 296
## 
## Fixed effects:
##                               Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)                  -0.002218   0.025542 488.541227  -0.087    0.931
## scale(Female)                 0.442976   0.025637 493.917911  17.279  < 2e-16
## ynFemaleNot F                 0.005273   0.020546 493.014571   0.257    0.798
## scale(Female):ynFemaleNot F  -0.103148   0.020841 493.025884  -4.949 1.02e-06
##                                
## (Intercept)                    
## scale(Female)               ***
## ynFemaleNot F                  
## scale(Female):ynFemaleNot F ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(F) ynFmNF
## scale(Feml) -0.058              
## ynFemaleNtF -0.319  0.073       
## scl(Fm):FNF  0.072 -0.322 -0.225
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00218099 (tol = 0.002, component 1)
ggpredict(m, c("Female", "ynFemale")) %>% plot()

m <- lmer( scale(selfResp) ~ scale(Female)*ynFemale*streng + ( scale(Female) | subID) + ( 1 | traits), data = GenLong)
summary(m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## scale(selfResp) ~ scale(Female) * ynFemale * streng + (scale(Female) |  
##     subID) + (1 | traits)
##    Data: GenLong
## 
## REML criterion at convergence: 344490.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.8971 -0.6504 -0.0184  0.6422  4.3379 
## 
## Random effects:
##  Groups   Name          Variance Std.Dev. Corr 
##  subID    (Intercept)   0.04738  0.2177        
##           scale(Female) 0.04604  0.2146   -0.21
##  traits   (Intercept)   0.14360  0.3789        
##  Residual               0.59616  0.7721        
## Number of obs: 146472, groups:  subID, 495; traits, 296
## 
## Fixed effects:
##                                     Estimate Std. Error        df t value
## (Intercept)                          0.36880    0.17061 507.46777   2.162
## scale(Female)                       -0.32936    0.16831 508.03267  -1.957
## ynFemaleNot F                       -0.12741    0.21774 490.92729  -0.585
## streng                              -0.05401    0.02456 490.93140  -2.199
## scale(Female):ynFemaleNot F          0.14801    0.21476 491.02500   0.689
## scale(Female):streng                 0.11243    0.02422 491.03362   4.642
## ynFemaleNot F:streng                 0.01859    0.03182 490.92751   0.584
## scale(Female):ynFemaleNot F:streng  -0.03498    0.03138 491.02512  -1.115
##                                    Pr(>|t|)    
## (Intercept)                          0.0311 *  
## scale(Female)                        0.0509 .  
## ynFemaleNot F                        0.5587    
## streng                               0.0283 *  
## scale(Female):ynFemaleNot F          0.4910    
## scale(Female):streng               4.43e-06 ***
## ynFemaleNot F:streng                 0.5592    
## scale(Female):ynFemaleNot F:streng   0.2655    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) scl(F) ynFmNF streng sc(F):FNF sc(F): ynFNF:
## scale(Feml) -0.198                                             
## ynFemaleNtF -0.770  0.155                                      
## streng      -0.989  0.199  0.775                               
## scl(Fm):FNF  0.155 -0.770 -0.201 -0.156                        
## scl(Fml):st  0.199 -0.989 -0.156 -0.201  0.775                 
## ynFmlNtF:st  0.763 -0.153 -0.996 -0.772  0.200     0.155       
## scl(F):FNF: -0.153  0.763  0.200  0.155 -0.996    -0.772 -0.201
ggpredict(m, c("Female", "ynFemale")) %>% plot()